BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling
Authors: Hao Li
Yu-Hao Huang
Chang Xu
Viktor Schlegel
Renhe Jiang
Riza Batista-Navarro
Goran Nenadic
Jiang Bian
Venue: ICML 2025 poster (2025)
Keywords: Multi-agents Systems
Time Series Generation
Large Language Models
Primary area: applications->time_series
TL;DR: A text-controlled time series generation model and a LLM-based multi-agents framework for text instructions building.

Abstract: Time-series Generation (TSG) is a prominent research area with broad applications in simulations, data augmentation, and counterfactual analysis. While existing methods have shown promise in unconditional single-domain TSG, real-world applications demand for cross-domain approaches capable of controlled generation tailored to domain-specific constraints and instance-level requirements. 
In this paper, we argue that text can provide semantic insights, domain information and instance-specific temporal patterns, to guide and improve TSG.  We introduce ``Text-Controlled TSG'', a task focused on generating realistic time series by incorporating textual descriptions.
To address data scarcity in this setting, we propose a novel LLM-based Multi-Agent framework that synthesizes diverse, realistic text-to-TS datasets. Furthermore, we introduce Bridge, a hybrid text-controlled TSG framework that integrates semantic prototypes with text description for supporting domain-level guidance. This approach achieves state-of-the-art generation fidelity on 11 of 12 datasets, and improves controllability by up to 12% on MSE and 6% MAE compared to no text input generation, highlighting its potential for generating tailored time-series data.

BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling
Hao Li Li Yu-Hao Huang 13 Chang Xu Viktor Schlegel 24
Renhe Jiang? Riza
Goran 2enadic Jiang Bian
Abstract
2020), healthcare (Li et al., 2023) and environmental science (Hasnain et al., 2022). For example, realistic synthetic med- ical electrocardiogram (ECG) patterns can be used to train medical residents (Pöhl et al., 2025; Hong & Chun, 2023), while simulating regional electricity usage can be used to stress test the power grid (Westgaard et al., 2021). Although some remarkable works (Huang & Deng, 2023; Bao et al., 2024) have been done for TSG, showing promising results in generating realistic and coherent time series (TS), most of them focus on the basic setting-unconditionss single domain generation. However, in real application there are specific constraints or requirements for the gener- ated TS to be met, such as specifying domain-specific char- acteristics, incorporating prior knowledge (Yuan & Qiao, 2024), or satisfying operational constraints (Coletta et al., 2023). For instance, it may be necessary to generate ECG patterns that respect individual patient profiles or capture specific disease conditions (Schlegel et al., 2023). There- fore, the current status TSG, while demonstrating strong foundational performance, leaves significant gap for ad- dressing more complex, constrained generation tasks that are crucial for real-world applications.
Time-series Generation (TSG) is a prominent research area with broad applications in simu- lations, data augmentation, and counterfactual analysis. While existing methods have shown promise in unconditional single-domain TSG, real-world applications demand for cross-domain approaches capable of controlled generation tai- lored domain- constraints and instance- level requirements. In this paper, we argue that text can provide semantic insights, domain infor- mation and instance- spencccic temporal patterns, to guide and improve TSG. We introduce "Text- Controlled TSG", a task focused on generating re- alistic time series by incorporating textual descrip- tions. To address data scarcity in this setting, we propose novel LLM-based Multi-Agent frame- work that synthesizes diverse, realistic text-to-11 TS datasets. Furthermore, we introduce BRIDGE, a hybrid text-controlled TSG framework that inte- grates semantic prototypes with text description for supporting domain-leve guidance. This ap- proach achieves state-of-the-arr generation fidelity on 11 of 12 datasets, and improves controllability by up to 12% on MSE and 6% MAE compared to no text input generation, highlighting its potential for generating tailored time-series data. Our code is available at: cicrosott/Ts www.of////meraaa....
scenarios,
Some prior work on cross-domain TSG has explored var- ious ways to meet specific generation needs, with most focusing on leveraging domain information to control the generation process. Some approaches rely on explicit do- main labels during training (Huang al., 2024; Kollovieh et al., 2024), treating the task as conditional generation problem. This allows users specify the domain during inference. However, this method is limited as it struggles with unseen domains and becomes inefficient when the num- ber of domains is large. Other methods incorporate specific information through natural language (Zhou et al., 2024; Liu et al., 2024d), but they operate the domain level, thus failing to provide detailed fine-grained and instance-specife control, which is essential for more accurate and tailored TSG, highlighting significant gap in the field.
1. Introduction
High-quality Time Series Generation (TSG) is an impor- tant task in various domains, including finance (Sezer et al.,
"Equal contribution 'Microsoft Research 'The University of Manchester, UK Nanjing University, China "Imperial Col- lege London, Imperial Global Singapore, Singapore 'The Uni- versity of Tokyo. Japan. Correspondence to: Chang Xu <chanx @ microsoftt.co...
In this work, we investigate the challenging yet practical re- search problem of achieving instance-level controlled TSG capable of generalising to unseen domains. Inspired by the recent success of controlled content generation in images
Proceedings of the 4rnd International Conference on Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
Work done during Hao Li and Yu-Hao Huang's research in- ternship at Microsoft Research.

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Bootstrapping Text to Control Time-Series Generation
(Zheng et al., 2023) and videos (Liu et al., 2024e), where texts are used source of control which facilitates captur- ing complex patterns and semantic relationships, we argue that using text to provide semantic insights- such as domain information and instance-specific temporal patternscoull enhance and guide TSG. However, using text for controlled TSG presents two key challenges that need to be addressed in order to fully leverage its potential.
comprehensive than those generated through traditional text- generation methods.
To address the second challenge, we adopt a hybrid text- enhanced time-series generation strategy. This approach incorporates semantic prototypes (Huang et al., 2025) to extract implicit domain features from TS, complementing text-based conditioning that provides explicit domain in- formation for diffusion model. As result, the proposed model achieves SOTA performance across multiple datasets on fidelity of the generation results and demonstrates con- trollability in both in-domain and out-of-domain settings.
(i) Limited availability of high-quality text-TS pairs: To train a TSG model that can be controlled by text, we re- quire paired data, where each time series is associated with a detailed text description. However, most available text data only provides bigh-level domain descriptions, lacking granular, -pscince-specific information such as trends, fluc- tuations, or the behavior of individual data points (Liang et al., 2024). The first challenge, therefore, is determining what specific text information is useful for controlling gen- eration and how to obtain such detailed text. We explored rule-based methods to generate individual-level text descrip- tions (Harris& & Zaki, 2022), but it did not lead to significant performance improvements (as shown in Appendix A.8), suggesting that more sophisticated approach is needed.
To summarize, this paper presents the following novel con- tributions: First, we introduce a multi-agent framework for creating a text-controlled TSG dataset. Our numerical experiments show that textual descriptions provide valu- able information for time-series models. Second, using this dataset, we analyze the impact of different types of time- series descriptions, advancing the understanding of how LLMs can assist in time-series prediction and generation. Third, we propose BRIDGE, a novel framework for text con- trolled TSG through diffusion models. Our approach excels in generating highly controllable time-series, outperforming baseline models across 11 out of 12 datasets, and highlights its potential for tackling complex, real-world tasks, with promising applications in healthcare, finance, and beyond.
(ii) Bridging discrepancy between text and time-series data for controlled TSG:
Text and time-series (TS) data exhibit significant differences in both modality and granularity. Text conveys information via a fixed vocabulary of discrete tokens, while time series data is continuous, which leads to inherent mismatches. This disparity may render text too coarse fully capture domain- specific patterns and characteristics, posing challenges in achieving precise domain-level control due to its incomplete or oversimplified representations. Meanwhile, text can pro- vide detailed, instance-specific descriptions that are crucial for nuanced control, requiring careful and dedicated design to align text with TS features effectively.
2. Related work
Using Text for Time Series Modeling: Text-based ap- proaches have shown promise in enhancing various time series tasks, including forecasting (Jin et al., 2023; Gru- ver et al., 2023; Chen et al., 2023; Xue & Salim, 2024; Zhang et al., 2024b), elassification (Xie et al., 2023; Lopez- Lira & Tang, 2023), and event prediction (Gunjal & Dur- rett, 2023; Shi et al., 2024). While these studies primar- ily focus on leveraging text to guide or interpret existing time series, text-to-time series generation remains under- explored. GenG (Zhou et al., 2024) initiates this direc- tion with a two-stage pipeline, but is limited to specific domains and lacks instance-level control. Time-MMD (Liu et al., 2024a) presents a large-scale paired dataset for multi- domain text-time series forecasting. However, its step-level annotations focus local transitions, limiting its applica- bility to modeling or evaluating alignment over extended temporal sequences.
To address the first challenge, we propose role-based collaborative multi-agent framework designed to generate high-quality datasets for text controlled TSG. We argue that the process of automatically identifying textual descriptions for TS parallels prompt optimization for large language mod- els (LLMs), where variations in prompt design significantly affect performance (T al., 2024). Inspired by the success of prior work (Zhou et al., 2023b; Liu et al., 2024c: Guo et al., 2024), our framework consists of three key compo- nents: Text Template Generation, Automatic Evaluation, and Teedback-driven Refinement. This process ensures continu- ous improvement through feedback and synthesis, achieving a holistic optimization of the generated text tailored to the TS at hand. Experimental evaluations reveal the effective- ness of the proposed framework, achieving at least 15% performance improvement in MAE compare to the unre- fined text, while producing outputs that are notably more
Conditional Time Series Generation: Diffusion-based models have emerged as powerful paradigm for condi- tional time series generation. A number of recent works employ denoising score matching and continuous-time dif- fusion to enable flexible, probabilistic generation (Tashiro et al., 2021; Shen & Kwok, 2023; Narasimhan et al., 2024; Huang et al., 2024; Kollovieh et al., 2024; Shen et al., 2024;
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Bootstrapping Text to Control Time-Series Generation
Fan et al., 2024; Deng et al., 2025; Hou al., 2024) These methods explore range of network backbones, including score-based transformers (Yuan & Qiao, 2024), structured state space models (Alcaraz& & Strodthoff, 2023), and con- strained denoising objectives (Coletta et al., 2023). Some also incorporate seasonality and trend decomposition as ex- plicit inductive biases (Yuan & Qiao, 2024). Despite their expressiveness. most models operate within single-domain scenarios and rely on fixed conditioning formats. One of the works with cross-domain focus is TimeDP (Huang et al., 2025), which introduces the use of prototypes to build 'soft prompts" for guiding cross-domain generation. However, its coarse-grained control makes it challenging to achieve personalized text-controlled TSG.
Additionally, we tried to leverage GPT-4o and incorporated Beasonal-TTrend Decomposition using Loess (STL.) (Cleve- land et al., 1990) to preprocess time-series data by decom- posing it into trend, seasonal, and residual components, making the TS data easier for the model to interpret. How- ever, the resulting texts were overly simplistic and failed to capture the complex patterns and domain-specific nu- ances inherent in time-series data (results in Appendix A.8), Therefore, we further explore leveraging more diverse and enriched sources to generate text capable of effectively as- sisting time-series tasks.
To this end, We propose a multi-agent system to automat- ically generate and iteratively refine TS textual descrip- tions. As shown in Figure 1, the proposed framework comprises three key components: Step 1: Text Template Generation, which focuses on collecting and extracting text templates to construct initial textual descriptions; Step 2: Automated Evaluation, designed to assess the effectiveness of the descriptions in supporting downstream tasks; and Step 3: Feedback Breedback-Driven Refinement, which improves the textual descriptions based on evaluation metrics. Steps 2 and 3 will alternate and iterate until the agent system determines the output is sufficiently refined or predefined iteration limit is reached. Throughout the iterations, the agents refine set of general- purpose text templates, which are designed to be dataset-agnostic and easily adaptable to new domains and datasets. Subsequently, these refined templates are utilized to generate textual descriptions for TSG tasks.
3. Problem Formulation
Time series often span multiple domains, each with unique temporal dynamics. Let D = [D1, D2..... D& denote a set of domains, where each domain D; is associated with a collection of time series X; = {xe | t = 1,2,.... .T. and X2 E Rdd represents a d-dimensional vector at timestamp t. Our aim is to generate realistic time series while capturing domain-specific patterns and adhering to constraints speci- fied by textual descriptions. Formally, the goal is to learn generative function: fa (D,1,2) - X, where I represents a textual prompt describing characteristics such as trends or periodicity, and 2 is a latent variable sampled from a prior distribution. The output x a time series that aligns with both domain- specific patterns and textual conditions.
Step 1 Text Template Collection: As noted in previous work (Merrill et al., 2024), generating fine-grained text de- scriptions remains a challenging task due to the limited availability of extensive data resources, while also avoiding the potential information leakage that could occur when interacting with external sources while generating text de- scriptions for a single data. To address this limitation, we adopt template-basee approach that standardizes the narra- tive of key TS information. Starting with variety of initial queries, we first collect articles, news, and reports that de- scribe TS data. Inspired by ReAct (Yao et al., 2023), we propose single-agent framework, which prompts LLMs to generate dynamic reasoning traces for collecting candidates and actions to interact with external environments (e.g.. Google, Wikipedia) in an interleaved manner (Madaan et al., 2023) (Framework pipeline can be find in Appendix A.3). The agent decomposes the query into sub-questions, using external tools to answer each sub-question iteratively until all are addressed. Afterwards, another LLM extracts general TS templates from the collected documents, thus curating a set of 50 general- purpose templates. To ensure broad appli- cability across domains, -ppesst- details are carefully excluded through combination of prompting techniques and human verification. During the dataset construction phase, an LLM is employed to fill these templates with
Adhering to the chamnel-indeppnndnt setting (Nie et al., 2023) that is widely accepted by recent researches, we for- mulate the problem studied in this paper in a uni-variate time series generation manner to handle the heterogeneity of time series in terms of dimension (Woo et al., 2024).
4. Methodology
We propose a unified framework for text-controlled time series generation, which consists of two main stages: text- to-time series data preparation and text-to-time series data generation. The overall architecture is illustrated in Figure 1, where both stages are tightly coupled to facilitate high- quality, controlled time series synthesis.
4.1. Text-to-Time Series Data Preparation
To address the scarcity of high-quality text-to-time series paired datasets, we investigate how to generate effective textual descriptions of time series data create high-quality TS-text-paired datasets. Initially, we explored some straight- forward methods, such as rule-based approaches that relied on simple trend-related terms (c.g., "increasing", "decreas- ing") and degree modifiers (e.g., "significant", "slight").
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Bootstrapping Text to Control Time-Series Generation
Text-to-Time Series Data Preparation
Query
Initial/Ravised Text Description
Evaa luntion
Team
Search
Reasoning
Observation
Checking
Time Series
LLM based Time Scries Forecasting Model
Time Series
Stage
Team
Stage Inter-group Discassioon
Reshuatio
Task Phanning
Stage 2 Intra-group Discussion
Time Series
Post Pronessing
Unseea Domain Time Series Construction
Text
Inktial Query
Heeaative ReAct Model
Estracted Template
Text Deseription
Oulpul
Multi-Agent System
Template Corpus
Rvaluation Result
Texl-to-Time Series Datasel
Step 1 Text Template Collection
Step 2 Automatic Evaluation Step 3 Feedback-driven Refinement
Dataset Synthesis
Text Controlled Time Series Generation
Provide Training and Inference Datasets
Semantic Protolypes
Assignment Module
Random Noise
E
Domain Time Series
Encoder
Conditioning
Diffusion Model
Text Description
LLM
Encoder
Synthetic Time
Figure Overview of the proposed BRIDGE framework, which consists of main stages: text-to-time series data preparation and text-to-time series data generation. The former consists of three steps: (I) collecting initial text templates from online sources, (2) performing automatic evaluation to assess the quality of generated descriptions, and (3) employing a feedback-driven refinement process via a multi-agent system to iteratively improve the textual outputs. The latter, i.e., the diffusion-based generation model, we adopt a hybrid strategy where (i) textual description is used as input. Meanwhile, (ii) we utilize the target domain's 's time series and extract its corresponding domain prototypes and their weights using Prototype Assignment Module. These prototypes and weights are also used as inputs and are fused with the textual description. This fusion serves the conditioning input for the diffusion model, which is applied perform text-controlled TSG.
domain-specific and sime-series-relatee details, resulting in initial textual descriptions tailored to the target domain. The detailed example can be viewed in Appendix A.6.
prompt off-the-shelf LLMs with saain-of-thought (CoT) reasoning (Wei et al., 2022), which enables the integration of text as an additional input modality (Liu et al., 2024b). For the detailed automatic evaluation pipeline, initial evalu- ation criteria and definitions, refer to Appendix A.4 and A.5. Additionally, we conducted evaluations on the impact of different text types TSG-result shown in Appendix J demonstrate that generated texts that improve LSTPrompt performance in TSF, also enhances TSG performance.
Our goal is to equip the sys- tem with the ability to evaluate generated text and provide iterative feedback. aligning with the broader objective of leveraging text to guide TSG. This is achieved by incorpo- rating is task conditioned on accompanying text, based the premise that higher-quality text improves performance. While straightforward approach might involve pre-training or fine-tuning existing models specifically for TSG, this would demand substantial computational resources. Such an approach would be highly resource-intensic require training fine-tuning every iteration.
Step 2 Automatic Evaluation:
Step 3 fredback-driver Refinement: Based on the ini- tial text templates and the automatic evaluation system, we further propose multi-agent collaboration system that sim- ulates the iterative refinement process of team of human prompt engineers, leveraging the demonstrated capability of LLMs to improve their outputs (Zhang et al., 2024a). As illustrated in Figure 1, the system operates through three stages: Stage 1 Task Planning (assigning tasks and mon- itoring progress), a manager agent orchestrates the work- flow, laying the groundwork for all subsequent stages. This agent assigns specific tasks independent teams, monitors
would
Because recent advancements in prompting strategies have demonstrated the potential of LLMs for zero-shot Time Series Forecasting (TSF) (Gruver et al., 2023), we use LST- Prompt (Liu et al., 2024b) and LLMTime (Gruver et al., 2023) as our evaluation backbone for a more compute- efficient alternative model training. Specifically, we

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Bootstrapping Text to Control Time-Series Generation
progress, and ensures alignment across iterations. Stage 2 Intra-group Discussion (independent teams iteratively refining outputs), two independent teams work iteratively to refine the text. Each team includes agents with specific roles: a planner who coordinates tasks, a scientist who ana- lyzes and identifies improvements, an engineer who imple- ments changes, and an observer who critiques the outputs. The teams refine their work through cycles until satisfac- tory output is produced. Stage 3 Inter-group Discussion (collaborative consensus building), the two teams' leaders, moderated by the manager, engage in a structured dialogue to compare and integrate their results. This collaborative process continues until a consensus is reached, producing a unified output. Once all initial templates are refined, the sys- tem extracts them into new set of templates in Stage 4 Post Processing. These final templates are filtered to remove du- plicates and ensure they do not contain any -ptaset-specific or sene--srres-speccfee information, resulting in general- ized template library. This library is fixed and remains unaltered during subsequent dataset construction, ensuring consistency and reusability. Further details about the system structure, processing pipeline, and output examples can be found in Appendices 1, A.2 and A.7.
We selected diffusion models (Ho et al., 2020; Lin et al., 2024) the backhone of our framework due their proven ability to generate diverse, high-quality data while effec- tively capturing complex data distributions. Recent suc- cesses of diffusion models in time-series forecasting (Rasul et al., 2021) and generation (Qian et al., 2024) further un- derscore their suitability for this task. The specific design of our architecture detailed in Appendix B.2.
4.2.1. DOMAIN-SPECIRE PROTOTYPE MATCHINNG
Relying solely on text descriptions often leads to insuf- ficient representation of Nomain-specifie patterns due to their poarse-grained and abstract nature. To address this, we draw inspiration from TimeDP (Huang et al., 2025), which introduces an automated approach to extracting TS domain features. We propose hybrid representation strat- egy that complements domain information using semantic prototypes. Semantic prototypes serve as "bases", represent- ing elementary features of time series such as trends and seasonality. These bases act as a shared "dictionary" across different domains, with each prototype vector serving as a "word" that encodes specific semantic features of TS data.
Specifically,, we introduce a set of vectors as time series prototypes P E R. for representing cross-domain time series common knowledge, where each prototype vector p E IR 1xd serves as the representation of a time series basis and No is the number of prototypes. Each time series sam- ple corresponds to a distinct allocation of these bases. The mapping from time-series samples to their respective alloca- tions highlights the importance of prototypes for individual instances and distinguishes among domains. We propose Prototype Assignment Module to extract domain-specifee prototype weights m. with domain characteristics expressed through the selection and weighting of these bases. Then, the extracted prototypes P and their corresponding weights m, denoted as (P, m)), are leveraged as supplementary information to enhance the text domain representation, en- riching its semantic understanding and cross-domain adapt- ability. During inference, samples from the target domain are used to extract prototypes and compute weights.
Excluding domain- or dptaset-specific information during template construction ensures their gen- eralizability to unseen domains. To demonstrate this, tem- plates generated with the methodology described in the previous subsection used only two datasets and are applied to 12 additional, entirely disjoint datasets in our TSG exper- iments (seen Section 5.2). During the dataset construction process, information specific to individual time series is ex- tracted, while domain-specific details, which are pre-defined in the dataset, remain consistent across all instances. Specif- ically, a standalone L.LM, separate from the multi-agent framework, is responsible for extracting statistical infor- mation from the time series and populating the templates accordingly. This process is entirely offline, with no reliance on external networks or the LLM's pre-trained knowledge. Once populated, the dataset remain static and unchanged throughout the TSG phase.
Dataset Synthesis:
4.2. Text Controlled Time Series Generation
4.2.2. MODEL TRAINING
Although text provides explicit details on trends, statistical properties, and domain-specifee information, its discrete na- ture contrasts with the continuous structure of time-series data, posing challenges for using text as a control mecha- nism. To address this, we present our framework for text- controlled TSG. As shown in Figure 1, the framework em- ploys hybrid prompt that integrates semantic prototypes which supplement coarse -rassee domain descriptions pro- vided by text, along with textual descriptions. This design captures shared patterns across domains and improves the model's 's generalization ability in unseen domains.
We integrate the semantic prototypes p with weights m and the embeddings of textual descriptions to construct hybrid prompt. This prompt is then fed into the cross- attention layers of diffusion-basee generation model, en- hancing the expressiveness of textual inputs. This enables precise and effective controlled generation. With the condi- tional denoising mechanism, the denoising objective using 6- parrameterization (Ho et al., 2020) can be written and sim- plified as:
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Bootstrapping Text to Control Time-Series Generation
5.1.3. EVALUATION METRICS
Assessing Fidelity of TSG: To evaluate the impact of text types, we use the Mean Absolute Error (MAE). For TC- TSG, we measure the Marginal Distribution Discrepancy (MDD) and Kullback-Leibler (K-L) divergence to quantify the realism of the synthesized data, with further details provided in Appendix G.
L= Billc - =] = BODEDT --.. - co, n. m, 1)1 (1)
where n denotes the denoising step. Please refer to Section 3 for symbol definitions. For implementation details, see Appendix B.1.
Assessing Controllability of TSG: To evaluate con- trollability in text-to-time series generation (TSG), we adopt the Joint Fréchet Time Series Distance (J-FTSD) (Narasimhan et al., 2024), which captures both local shape and global distributional similarity between the generated and reference sequences. This metric offers a more faithful measure of alignment than traditional point-wise metrics such as MSE or MAE, especially in cases where semantic similarity does not require exact numerical overlap. How- ever, quantitative metrics alone may overlook perceptual or contextual alignment. To complement this, we conduct a Human Evaluation, where outputs are ranked based on how well they reflect the intended text descriptions. We con- sider two settings: in HE-Rank, annotators evaluate only the outputs generated by different model settings along with the ground truth time series; in HE-Mixed, these candidates are presented alongside three randomly selected distractor time series from the test set. This design enables both focused and distractor-aware evaluation, capturing subtle differences (e.g., constant shifts) while assessing trend or pattern align- ment. Results are reported as average ranks across multiple evaluations. See Appendix H for details.
5. Experiment Setup and Result Analysis
Our experiments are designed to investigate the key claims of this paper: The feasibility of using text for TSG with BRIDGE, its generalisability to unseen domains (Sec- tion 5.2), and its capability to provide instance-level control in both in-domain and out-of-domain scenarios (Section 5.3). We also conduct a comprehensive analysis of textual descrip- tion types and agent strategies (Section 5.4 and 5.5), well as investigate the impact of BRIDGE'S ' parameter choices and configuration settings (Section 5.6).
5.1. Experimental Setup
5. BASELINES
We select SOTA methods for both TS generation and fore- casting tasks as baselines. For generation, we explore the performance of BRIDGE by comparing with condi- tional (TimeVOVAE, Lee et al. 2023) and unconditional approaches (TimegGAN, Yoon et al. 2019 GT-GAN, Jeon et al. 2022: Time VAE, Desai et al. 2021). For forecast- ing, our goal is to establish the realism of synthetic data. Here, we compare the performance of Time-LLM (Jin et al., 2023), I. LS (Chang et al., 2023) and TEMPO (Cao et al., 2024), G4TS (Zhou et al., 2023a). Detailed descriptions can be found in Appendix C.2. More details about Experi- ment Setup and Implementation can be found at Appendix D and Appendix E.
Time Series Forecasting: Following previous work (Wu et al., 2023), we measure the MSE and MAE for long- term forecasting. For short-term forecasting on the M4 benchmark, we adopt Symmetric Mean Absolute Percentage Error (SMAPE), Mean Absolute Scaled Error (MASE), and Overall Weighted Average (OWA) as evaluation metrics (Oreshkin et al., 2020).
5.2. Overall Generation Performance on Fidelity
5.1.2. DATASETS
5.2.1. PERFORMANCE ON IN-DOMAIN SETTING
We employ AirPassenger and Sunspots benchmark datasets for multi-agent system assessing the text types impact. We evaluate the effectiveness of BRIDGE on 12 in-domain datasets including Electricity, Solar, Wind, Traf- fic, Taxi, Pedestrian, Air, Temperature, Rain, NN5, Fred- MD, Exchange. These datasets have been widely used as benchmark datasets for TSG tasks and obtain from GluonTS (Alexandrov et al., 2020) and Monash Time Series Forecast- ing Repository (Godahewa et al., 2021). For the evaluation of unknown domains, we selected the stock and web dataset. Finally, We use III and M4 (Makridakis et al., 2018) as additional datasets for Time Series Forecasting task. (Details shown in Appendix F.2.)
Objective: We evaluated the overall fidelity of BRIDGE using 12 in-domain datasets. During inference, the text from the test set served as prompts, where the prototypes are generated by the training set.
As shown in Table 1, BRIDGE consistently outperforms existing baselines across a variety of datasets, demon- strating its robustness and versatility. In terms of MDD, BRIDGE (wo Text) achieves the best performance on most datasets, ranking second only on the Air dataset. For in- stance, on the Wind dataset, BRIDGE achieves an MDD of 0.316 + 0.031, significantly outperforming models like TimeVAE (0.943 + 0.008) and TimeGAN (1.115 + 0.159).
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Bootstrapping Text to Control Time-Series Generation
Dataset
BRIDGE
BRIDGE w/o Text
BRIDGE w/o Prototype
TimeVQVAE
TimeGAN
GT-GAN
TimeVAE
Electricity
0.220 0.070
0.202 + 0.066
0.277 = 0.068
1.763 + 0.088
2.443 + 0.765
2.026 + 0.280
3.306 + 0.044
Marginal Distribution Distmooz
Solar
375.531
0.001
375.908 1. 10.230
376.111 1 5.506
466.174 0.145
460.810 1 14.078
476.196
17.041
365,906 6.365
Wind
0.316 0.031
0.319 0.046
0.362 0.050
0.777 0.028
1.115 0.159
0.706 0.106
0.943 0.008
Traffic
0.254 3354++0.000 0.034
0.261 0.008
0.316 0.006
0.020
1.733 + 0.137
1.311 0.032
0
Taxi
0.386 1. 0.057
0.391 1 0.057
0.491 0.133
0.534 1 0.032
1.278 1. 0.168
1,118 1. 0.157
0.697 1 0.00T
Pedestrian
0.621 0.124
0.824 + 0.116
0.800 0.111
1.225 + 0.060
1.574 + 0.290
1,550 0.117
0.777 + 0.012
Air
0.447 1: 0.112
0.129
0.508 0.091
0.338
2,089 0.618
2,828 0.172
0.040
Temperature
0.342 0.010
0.345 114551000 0.019
0,408 0.053
0,943
1.164 1. 0.110
1,165 0.072
2.044 1 0.024
Rain
5,340 +: 0. 121
5,597 2 0.
2.018
4.213:0.122
10,937 = 4.039
6,473 + 1.207
4.131:0.00
NNS
0.591 0.029
0.628 1: 0.021
0.748 0.134
1.22410.000
2.738 1. 0.142
2.121 0.094
2.871 1. 0.045
Fred MD
0.045
0.271 0.056
0.359 0.097
2,932 0.133
4.028 0.130
4.026 0.087
2,902 0.215
Exchange
0.374 0.053
0.376 0.058
0,382 0,041
0.9933
1.533 = 0.122
1,353 0.072
1.331 + 0.002
Electricity
0.011 + 0.010
0.011
0,013 0.005
0.185 0.008
0.395 = 0.121
0,415 0.040
0,580 0.005
Solar
0.007 + 0.002
0.007 + 0.003
0.015 = 0.012
726---0.000
0.580 + 0.288
0.102 0.045
0.201 + 0.008
Wind
0.067 = 030
0.061 0.042
0.089 = 0.030
4.528 + 1.743
0.511 0.129
0.553 0.014
K- Divergeee
Traffic
0.013 0.004
0.013 0.005
0.017 0.004
0.145-+-0.00
2.134 0.952
1.108 0.171
0.212 0.006
Tixi
0.013 0.009
0.013
0.030 = 0.031
O. 0.004
1.160 + 0.651
0.663 0. 027
0. 0.120
Pedestrian
0.011 = +.0:+00000 0.009
0.011
0.020 = 0.00G
0.021
0,881 + 0.436
0.347 = 0.083
0.052 10022+0000
Air
0.022 0.017
0.018
0.019 0.011
0.017 0.000
0.588 0.369
0.506 0.091
Temperature
0.023 + 0.013
0.001
0.039 0.033
0.9
8.775 + 2.511
2.177 + 0.323
0.076
Rain
0.006 1. 0.001
0.007 0.0001
0.009 0.003
0.006 0.002
0.383 1 0.089
0.462 0.056
0.175 0.011
NN5
0.010 0.008
0.013
0.016 0.007
0.603
4.054 + 1.592
1.372 0.180
1,284 + 0.058
Fred-MD
0.024 0.019
0.028 0.029
0.033 0.029
0.712
6.371 + 1-455
3.500 0.299
0.376
Exchange
0.083 0.056
0.088 0.065
0.088 0.077
1,984 1 0.836
4.376 0.664
1,583 0.932
2.011 0.433
Table 1: Generation results on various univariate datasets, evaluated using Marginal Distribution Distance (MDD) and K-L divergence (K-L). Lower values indicate better performance. Best results are in red, and second best blue.
The KL divergence results further underscore BRIDGE'S capabilities. BRIDGE achieves the lowest KL divergence almost all datasets, ranking second only on the Wind and Air datasets. On the Electricity dataset, BRIDGE achieves a KI. divergence of 0.011 + 0.010, substantially better than BRIDGE (w/o Tcxt) at 0.014 + 0.013, and significantly out- performing baselines such as VQVAE (0.185 = 0.018) and TimeGAN (0.395 + 0.121). Notably, even without text conditioning, BRIDGE often secures the second-best per- formance, highlighting the strength of its core architecture. For example, on the Pedestrian dataset, BRIDGE (w/o Text) achieves the second-best KL. divergence of 0.011 + 0.009, only slightly behind the top-performing model. We also show that BRIDGE can generate realistic synthetic data for forecasting downstream tasks in Appendix I.
MDD
K-L
Methods 5-shots
10-shots
5-shots
10-shots
TQVA
3.502
3.514
1.487
3.785
TimeGAN
3.834
3.765
14.347
13.823
Stock
GT-GAN
3.653
3.474
10.971
8.855
TimeVAE
3.738
3,338
6.048
4.479
BRIDGE
3.477
3.112
3.249
2.827
TOVEV
9.630
10.012
1.665
1,213
TimeGAN
8.304
8.122
4.343
3.236
Web
GT-GAN
8.018
8.936
10.037
10.915
TimeVAE
10.
10.211
3.332
1.845
BRIDGE
8.085
7.995
0.905
0.876
Table 2: Few-shot Performance of Unseen domain. We compare the proposed methods and baseline on 5,10-shots Best results are highlighted in bold face.
5.2.2. PERFORMANCE ON UNSEEN DOMAIN SETTINGS
We evaluated the performance of BRIDGE in unseen domains, training on 12 in in-domain datasets and testing on two out-of-domain datasets. In this setting, the few-shot scenario established the prototypes, while the text functioned as the control condition for generation.
Objective:
enhancing its generalization capability.
5.3. Controlling Generation Performance
1.3.1. PERFORMANCE ON IN-DOMAIN SETTINGS
Objective: We evaluated the controllability of BRIDGE on 12 in-domain datasets using J-FTSD (Narasimhan et al., 2024) and human evaluation, while keeping all other settings consistent with Subsection 5.2.
Our model demonstrates superior robustness in both MDD and K-L metrics, outperforming the baseline models in most cases (Shown in Table 2). Specifically, it achieves the best results for general MDD and K-L scores at both 5-shot and 10-shot settings. Furthermore, the performance improvement with additional examples suggests that the model effectively leverages leamed semantic prototypes to recall more accurate domain and pattern information,
As shown in Table 3, the proposed BRIDGE consistently outperforms the two ablated variants in most of datasets, according to both the J-FTSD score and human evaluation metrics (HE and HE+3). The removal of textual input leads
7

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Bootstrapping Text to Control Time-Series Generation
Electricity HE-Rank
Solar HE-Rank
Wind HE-Rank
Traffic HE-Rank
J-FTSD
HE-Mixed
J-FTSD
HE-Mixed
J-FTSD
HE-Mixed
J-FTSD
HE-Mixed
BRIDGE
0.538
2.3
2.4
0.295
2.6
2.8
5.011
1.8
1.8
0.570
1.4
144
w/o Prototype
1.164
3.3
3.4
0.322
3.2
3.3
6.843
3.1
3.2
0.597
2.1
2.1
wfo Text
1.821
3.4
3.5
0.330
3.2
3.4
6.935
4.0
4.2
0.611
4.2
4.4
Taxi HE- Rank
Pedestrian HE: Rank
Air HE: Rank
Temperature HE Rank
J-FTSD
HE Mixed
J-FTSD
HE Mixed
J-FTSD
Mixid
J-FTSD
HE: Mixed
BRIDGE
0.974
1.4
1.4
0.488
2.2
2.6
0.654
2.7
2.8
3.977
2.4
w/o Prototype
1.037
3.1
3.6
0.662
2.6
0.817
3.0
4.613
2.6
3.1
w/o Text
1.312
4.2
0,550
3.3
0.677
3.3
3.1
4.708
3.6
3.9
Rain HE: Rank
NNS HF: Rank
Fred-MD HF. Rank
Exchange HE- Rank
J-FTSD
HE Mixed
J-FTSD
HE Mixed
J-FTSD
Mi-Mixed
J-FTSD
MixMixed
BRIDGE
0.147
1.9
1.9
0.972
2.5
0.260
2.5
2.8
1.581
1.9
w/o Prototype
0.111
3.2
3.2
1.115
3.1
3.5
0.311
2.7
3.0
1.841
3.1
3.1
w/o Text
0.151
3.7
3.7
1.192
3.4
3.8
0.423
3.8
4.2
1.738
3.7
3.7
Table 3: Text control performance of in-domain settings. Measured by J-FTSD and human evaluation.
to the most severe performance drop. particularly in human that text is essential for pro- ducing outputs that align with intended semantics. Without textual guidance, the model tends to generate less coherent and less interpretable sequences. For example, on the Traf- fie and Pedestrian datasets, HE scores increase by over 3 points without text, indicating poor alignment. The "w/o Prototype" setting shows moderate degradation, suggesting that while prototype representations enhance fine-grained alignment and structure, they are less critical than textual de- scriptions. Notably, the gap especially visible on datasets with high temporal variability such as Wind and Temper- ature. These results confirm that both components- and prototypes-plae complementary roles in generating realistic and controllable time series.
I-FTSD
HF--Rok
Hi-Mised
J-FTSD
Hi--Rnnk
Mi-Mised
BRIDGE
7-483
2.8
2.8
5.521
2.7
3.2
wil Protorype
7.687
2.8
3.5
5.752
3.1
3.5
Teat
4.0
6.302
3.2
Table Comparison of text control performance of different settings on unseen domains. Measured by J-FTSD and human evaluation.
5.4. Performance Analysis on Controlling Text
Objective: We conducted a comprehensive analysis of the influence of text types. Specifically, we performed TSF experiments on two benchmark datasets to assess the impact of different text types.
5.3.2. PERFORMANCE ON UNSEEN DOMAIN SETTINGS
Conciseness leads to better performance. Table 5 shows that concise text inputs outperform overly detailed ones, which can mislead the model. This is particularly evident in the case of w/o instance context", where the MAE im- proves by 1.6 (compared to Initial text") on the AirPassen- ger dataset, indicating that generating text that fully aligns with human preferences remains a challenging task. No- tably, when it comes to longer sequence length, the con- text provides more useful information (48.64 59.91 on Sunspots). Clearly specifying ing the length of the predic- tion/generation can make the model's performance more stable. This can be seen from the performance of "w/o statis- tics". After providing a clear sequence length and statistical values, the model's performance improves. Background in- formation helps the model. Similar to the findings of other works (Jin et al., 2023; Merrill et al., 2024), background information can significantly improve the model's perfor- mance. This is likely because retrieving the pre-trained knowledge from the LLMs can offer additional contextual information as support. Direct pattern descriptions are more effective than detailed trend descriptions. As men- tioned Appendix A.8, when attempting to decompose
Objective: Same to the in-domain setting, we evaluated the controllability of BRIDGE on 2 unseen domain datasets.
Table 4 highlights the structural contributions of text and prototype components in unseen domains. The full BRIDGE model achieves the lowest J-FTSD and most favorable hu- man evaluation scores across both Stock and Web datasets, indicating strong generalization beyond the training distri- bution. Removing text input causes the most notable perfor- mance drop, especially in J-FTSD and HE+3, confirming that textual guidance is critical for semantic alignment and coherent generation. In comparison, the "w/o Prototype" variant shows only moderate degradation, particularly in HE but not in J-FTSD, suggesting that prototypes help en- force structural consistency but are less essential than text for global control. These results support a complementary division of labor: text governs high-level semantics, while prototypes refine local dynamics.
8

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Bootstrapping Text to Control Time-Series Generation
Text Types
AirPussenger
Sunspots
Text Types
AirPassenger
Sunspots
LLMTTime
LSTPrompl
LLMTime
LSTPrrmpt
L.MMTime
PSTPpompt
L.MTTme
I.STPrompt
Rule-hased Test
52.41
20.08
63.92
51.61
Agent Strategies
Initial Tour
49.36
15.12
59.88
49.71
Multi-Agen Toams
40.94
12.39
48.64
42.37
Refindd Test(uuss)
12.30
48
42.37
wlo Insionce Contest
4I.96
13.54
14.33
14.23
Single (Micro)
44.27
14.22
56.55
45,70
w/o Background
11.63
11.77
56,81
16.07
Single (Macro)
42.57
13.83
54.51
45.01
Statisiical Context
13.41
54.24
47.12
w/o Puttern
4.1.36
11.52
55.16
16.84
Table 6: Zero-shot TS forecasting performance under dif- ferent multi-agent strategies. MAE is reported for each configuration; lower values indicate better performance.
w/o Patter--Sttiitic
44.30
14.27
56,80
45.63
Buseline (Liu 2024b)
15.775
15.00
59.91
17.59
Table 5: Zero-shot time-series forecasting performance (Mean Absolute Error, lower is better) using different tex- tual descriptions across two datasets: AirPassenger and Sunspots.
agent system, as well as the performance of different set- tings, varying protolype quantities, and using different lan- guage models as the text encoder in BRIDGE.
the TS into seasonal, trend, and residual components, the model's performance did not show significant improvement. After multiple iterations, the most effective method was to provide the overall upward/downward trends and explicitly identify the top l extreme points. In addition, we further investigated the impact of revised text types on generation performance. These results support our findings in the TSF task, such as rule-based contextual information may bring more confusion, detailed in the Appendix J.
As shown in Table 1, the BRIDGE outperforms other vari- ants most datasets, as shown by its superior MDD and K-L. divergence scores. Removing text input (BRIDGE w/o Text) leads to higher MDD and K-L divergence in nearly all datasets, highlighting the importance of text for improv- ing generation quality. In the NN5 dataset, MDD increases from 0.591 to 0.628, and in the Exchange dataset, K-L diver- gence rises from 0.083 to 0.088. The removal of prototypes (BRIDGE w/o Prototypes) causes the most significant per- formance decline. As shown in Table 10 in Appendix K, the number of prototypes significantly influences perfor- mance: more prototypes provide richer reference patterns that enhance generation quality. However, we also observe that increasing the number of prototypes beyond 16 brings only marginal improvement. Therefore, we adopt 16 pro- totypes a practical trade-off between performance and computational efficiency. Moreover, we explored the im- pact of pre-training knowledge from LLMs (Dubey et al., 2024). The results show that the larger models have a slight change in performance, but it is not significant, indicating that the pre-training knowledge has a minor influence on performance (shown in Appendix L). For visualization of se- mantics on various datasets, please refer to the Appendix N.
5.5. Performance Analysis on Individual Components of Multi-Agent System
Objective: We further evaluated different agent strategies on refined text. In the strategy experiments, the Macro approach involves a single team making high-level infor- mation adjustments, while the Micro approach emphasizes fine-ge lieeggrineed details. The Multiple Teams strategy represents a collaborative setting where two teams work together to accomplish the task.
As shown in Table 6, the multi-agent team strategy consis- tently achieves lower MAE than both micro- and macro- level single-agent approaches across datasets and text types. This suggests that collaborative generation captures a broader range of relevant patterns, leading to more effec- tive forecasting guidance. Between single-agent strategies, macro-level descriptions outperform micro-level ones, in- dicating that concise, high-level summaries are easier for models to utilize than overly detailed inputs. Notably, all strategies perform better under the LSTPrompt setting, high- lighting its stronger alignment with model input expecta- tions. These findings underscore the importance of struc- tured, semantically aligned textual inputs in enabling robust zero-shot forecasting performance.
6. Conclusion
In this work, we explore the potential of using text to guide TSG. We propose a multi-agent system for refining tex- tual descriptions and a text-controlled TSG model. Experi- ments show that concise text improves text-controlled per- formance, with model surpassing baselines, especially in few-shot learning, demonstrating strong generalization. Additionally, results indicate that the designed semantic prototypes effectively complemented domain information.
5.6. Ablation Study
Objective: We further conducted ablation experiments to explore the impact of different components in the multi-
These findings lay the groundwork for advancing human- preferred text-controlled TSG.

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Bootstrapping Text to Control Time-Series Generation
Impact Statement
Berger, V. W. and Zhou, Y. Kolmogorov-snimnox test: Overview. Wiley statsref: Statistics reference online, 2014.
This paper presents work aimed at advancing the field of Time-series Generation (TSG) by introducing a novel frame- work, BRIDGE, which incorporates text to guide and im- prove time series generation. The potential societal conse- quences of our work are significant, as TSG plays crucial role in applications ranging from simulations to counter- factual analysis. By enabling controlled generation of time series tailored to Nomain--peciffe constraints and instance- level requirements, our approach has the potential drive innovations in fields such as healthcare, finance, and climate are publicly avail-
Cao, D., Jia, F., Arik, S. Ö., Pfister, T., Zheng, Y., Ye, W., and Liu, Y. TEMPO: prompt-based generative pre- trained transformer for time series forecasting. In ICLR. OpenReview.nec, 2024.
Chang, C.,, Peng, W., and Chen, T. LLM4TS: two-stage fine-tuning for time-series forecasting with pre-trained llms. CoRR, abs/2308.08 08446, 2023.
modeling. Ethically, the datasets able research datasets that have already undergone ethical review.
Chen, Z., Zheng, L. N., Lu, C., Yuan, J., and Zhu, D. Chat- gpt informed graph neural network for stock movement prediction. CoRR, abs/2306.0377 2023.
Acknowledgement
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Viktor Schlegel is part of the IN-CYPHER programme and supported by the National Research Foundation, Prime Minister's Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) pro- gramme. We are especially grateful to Microsoft Research for providing the computational resources that supported this work. We also thank Research IT at the University of Manchester for access the Computational Shared Facility, and the Imperial College Research Computing Service? for additional computing support.
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Bootstrapping Text to Control Time-Series Generation
Text
Evaluation Metrics
Background:<Datee Description>
The metrics results are: Time Series Evaluation
Statisties:
<Min>, <Max., "MMdiinn,=SSanduue Deviation>
Trend: <Tend description>
Metrics < Text Evaluation Metrics>
Jem: <Sequence Length>>
Time Series
Forecasting [148,188 136,119,104....
112.118..192.1299211133333.
Refined Text
Edit, add delete any sec-tion of
XN
the text description to make the
Engineer
given evaluation metrics
performance better...
Critic
Planner
Note:
** You only modify, enha-nce
delete the content...
Scientist
R2
You can also propose more objective indicators...
s- You can't modify the model. historical and future TS...
Scientist
Critic
Planner
Engineer
Manager
Step 3 Inter-group Discussion
Step 2 Intra-group Collaboration
Step Planning
Figure 2: Detail workflow of proposed multi-agent collaborative framework
A. Text Preparation
A.1. Multi-Agent Collaboration Framework Details
A.1.1. FRAMEWORK WORKFLOW
We propose structured, multi-agent collaboration framework designed to iteratively optimize text generation through systematic refinement. While the system is capable of operating with single team employing distinct strategies, our experimental results demonstrate that employing two independent teams yields superior outcomes in terms of both quality and diversity of generated outputs. As can be seen from Figure 2, the framework comprises three primary stages:
In Stage Task Planning. manager agent assumes responsibility for overseeing the workflow. This agent coordinates all subsequent actívities by distributing tasks and results from prior iterations ensure seamless progress and alignment among team members. The manager also defines the objectives for the teams, thereby establishing structured foundation for collaboration. Stage 2: Intra-group Collaboration constitutes the core of the system, wherein two independent teams of agents work concurrently to refine the given text. Each team is composed of four roles: a planner, scientist, an engineer, and an observer. The planner serves as the team leader, formulating strategies and supervising operations. The scientist analyzes the input data and formulates detailed optimization plans. The engineer executes these plans, generating improved text outputs. The observer critically evaluates the plans and outputs, raising questions identify shortcomings and potential improvements. Teams operate in iterative cycles, guided by the observer's critiques. This self-refining loop continues until the observer ceases to raise objections predefined maximum number of iterations is reached. Through this iterative process, each team independently produces refined output. In Stage 3: inter-group Discussion, the leaders of the two teams engage in structured dialogue moderated by the manager. This stage facilitates the integration of insights from both teams, encouraging comparative evaluation and collaborative refinement of their outputs. The discussion continues until a consensus is reached, resulting in a unified solution that incorporates the strengths of both teams.
The finalized output is then subjected Prot--rrcsssig. This phase includes validation step, where the text evaluated
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Bootstrapping Text to Control Time-Series Generation
against a predefined model ensure its quality and adherence to target metries. Approved outputs incorporated into formal dataset, expanding the training resources available for future tasks. Additionally, any templates developed during the process are added to general template library, enabling reusability and continuous improvement in subsequent data generation efforts.
A.1.2. STAGES OF COLLABORATION
Stage 1: Task Planning
Role Focus: Manager
The manager initiates the workflow and assigns responsibilities to the team leaders.
Stage 2: Intra-group Collaboration
Role Focus: Planner, Scientist, Engineer, and Observer
Teams execute their tasks, with internal cycles involving:
The scientist proposing plans.
The engineer implementing them.
The observer providing feedback until quality is satisfactory.
Stage 3: Inter-group Discussion
Role Focus: Manager and Team Leaders
Leaders from each team present their refined outputs to the manager.
Discussions between leaders and the manager lead to a unified, optimized output.
A.2. Example of Initial and refined text
This task focuses on predicting future values of a given time series based on historical data. The historical data shows periodic spikes in values, followed by declines, indicating a strong seasonal pattern. Notable fluctuations are observed at time points when values exceed 400, suggesting external influences. The average value of the historical data approximately 239.95, with a variance of 8271.86 indicating significant fluctuations around the mean. Future Data Projections indicate that expected values for the time series range between minimum of 310 and maximum of 622, with mean of 0.49 and standard deviation of 92.75, suggesting increased variability compared the historical period. The median value is projected to be around 453.34. Anticipated peaks are likely to occur at steps 5, 15, 25, and so on, while predicted dips are expected at steps 8, 18, 28, and similar intervals. Statistical Summary for the future time series reveals the following quartiles: the first quartile (Q1) is **, the median is **, and the third quartile (Q3) is **, Periodicity and Pattern Aligmment show consistent high values expected every 10 steps, with auto-correlation anticipated around these intervals, reflecting the cyclical nature observed in the historical data.
Initial Text:
This dataset provides monthly totals of US airline passengers from 1949 1960. The prediction length is 29 time steps. **Future Data Description:** Future data projections indicate that expected values for the time series range between **minimum of 310** and a **maximum of 622**, with a **mean of approximately 440.31 ** and a **standard deviation of about * suggesting variability similar to the historical period. The **median value** * projected to be around **419** The **first quartile (Q1)** is approximately **390.5**, and the **third quartile (Q3)** is approximately **488.5** Anticipated **peaks*** are likely to occur at **steps 5, 15, and 25** while predicted **dips** are expected at **steps 18, and 28** reflecting the cyclical patterns observed in the historical data. **Periodicity and pattern alignment** indicate consistent high values expected every ** 10 steps** with # couto-correlation** ** anticipated around these intervals, continuing the cyclical nature observed previously.
Iteration 1:
Iteration 2: Future Data Projections indicate that expected values for the time series range between a minimum of 0.004 and a maximum of 0.98, with a mean of 0.49 and a standard deviation of 0.29, suggesting increased variability compared to the historical period. The median value is projected to be around 0.45. Anticipated peaks are likely to occur at steps 5, 15, 25, and so on, while predicted dips are expected at steps 8, 18, 28, and similar intervals. Statistical Summary for the
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Bootstrapping Text to Control Time-Series Generation
future time series reveals the following quartiles: the first quartile (Q1) is 0.21, the median is 0.45, and the third quartile (Q3) is 0.70. Periodicity and Pattrm Alignment show consistent high values expected every 10 steps, with auto-correlation anticipated around these intervals, reflecting the cyclical nature observed in the historical data.
The air passengers dataset provides monthly totals of US airline passengers from 1949 to 1960. Data Projections indicate that expected values for the time series range between a minimum of 310 and a maximum of 622, with mean of 453.34 and a standard deviation of 92.75, suggesting increased variability compared to the historical period. Anticipated peaks are likely to occur at steps 5, 15, 25, and on, while predicted dips are expected steps 8, 18, 28, and similar intervals.
Iteration 3:
Refined Text: The air passengers dataset provides monthly totals of US airline passengers from 1949 to 1960. The prediction length is 29 time steps. Data Projections indicate that expected values for the time series range between minimum of 310 and maximum of 622, with mean of 453.34 and standard deviation of 92.75, suggesting increased variability compared to the historical period. Anticipated peaks are likely to occur at steps 5, 15, 25, and so on, while predicted dips are expected at steps 8, 18, 28, and similar intervals.
A.3. Pipeline for Collect the text candidate
Figure 3 shows how the single agent framework is proposed how to collect templates. While direct search for relevant content is feasible, it is constrained by a maximum of titles relevant to the query keyword. To overcome this, we aim to gather relevant candidates based on content similarity. For instance, a simple search for "time series generation" " might return its definition, but reasoning-enablee agent can plan what types of articles more likely to contain relevant content, thereby diversifying the search results.
Step 1: Single Agent Text Collection
Tools
Web
Reaching the iteration limit or good enough
Reasoning
Action
Observation
? Threshold
Initial Query
LLM
Sub- Sub-Query
Search
Checking
Collect Documents
Unsatisfactory collection of documents Continue Iteration
Figure 3: The pipeline of collect the document for extract template. Leverage the ReAct to inspire agent collect human-crad text about time series description. The framework stopped when collect enough document reach the max iteration limitation.
A.4. Detail of Automatic Evaluation
The system takes as input a historical TS alongside its textual description, using these as conditions to predict future TS values. This design leverages the intuition that historical TS serves as supplementary contextual information, simplifying the generation process while constraining the model's output space. This framework isolates the text's impact on forecasting accuracy, minimizing the influence of the TS data itself to better assess the quality of the textual input. To validate this approach, we conduct evaluations two widely studied datasets: the Air Passenger and Sunspots datasets, which offer diverse temporal patterns and domain characteristics. The key to our refined framework lies in the definition of the evaluation dimension, which directly influences the agent's 's ability to correct text-time series pair eтrоrs and provide high-quality feedback. On one hand, we define evaluation criteria that align with the modal characteristics of both text and TS, allowing the agent to consider both simultancously in order to correct the data. On the other hand, we also allow the agent to propose more suitable evaluation metrics.
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Bootstrapping Text to Control Time-Series Generation
A.5. Evaluation Dimensions
In this section, we detail the initial evaluation criteria and definitions used to assess the generated text and its impact on TS forecasting. These criteria are designed to align with the modal characteristics of both text and time series, enabling the agent to evaluate and correct the input-output pairs effectively. We consider both text and TS metrics. Specifically, we consider following dimensions for text:
Accuracy of trend description:
The description accurately identifies the steady increase in the time series.
Mention of seasonality: The description correctly notes the absence of seasonality in the data.
Completeness of information: The description covers the main aspects of the time series but could mention the exact rate of increase.
Clarity of description: The description clear and easy to understand.
we consider following for time series: Specifically, we consider Mean Squared Error (MSH) (Hurvich, 1988), Kolmogorov- Smirnov Test (K-S Test) (Berger & Zhou, 2014) and Wasserstein Distance (WD) (Panaretos & Zemel, 2019) for measuring the difference between the generated and target time series, and building a 5-point Likert scale for evaluate the text quality with 5 dimension (i.e. Accuracy of trend description; Mention of seasonality; Reference to external factors; Clarity of description; Completeness of information).
A.6. Initial Template Exact from Collect Template
The time series templates extracted from the collected corpus typically contain descriptions of key patterns such as trends, seasonalities, and changes over time. For example, a typical template could be structured as: Overall, [entity),
At the beginning. (detail.imitia)) As time progressed,
culminating
in {end description) } by {end time)
Additionally, the templates may include other relevant information, such statistical metrics (e.g., minimum, maximum, standard deviation), dataset information, degree words (e.g., dramatically, slightly) that describe the intensity of changes, and the time series length."
A.7. Example of Refined Text Template
1. T.ehe dataset_namn name dataset provides frequency totals of dataaddeacnnn description, from daare to end The prediction length is predic- tion length time steps. Data statistics indicate that expected values for the time series range between a mininum of value and a maximum of max_value, with mean of mean_ and a standard deviation of std_value, suggesting variability -umbliyyyyumaryyyyy compared to the beginning period. Anticipated peaks are likely to occur steps steps, while predicted dips are expected at steps dip-steps.
2.The dataset_ name dataset captures frequency measurements of data description collected between start_date and end date. Each series spans prediction Jength steps. Statistical indicators show values ranging from min_value to max value, with average of malue and a variability (standard deviation) of std_value. Notable shifts in the data occur around steps peak_steps (highs) and dip steps (lows), illustrating variabilty-sunmmrry summary trends.
3. With a focus on data data description, the dataset name dataset provides frequency records from start to end Analysis shows values peaking at max value dipping to a minimam of min value, while maintaining an average of mean value. The standard deviation of std_value highlights variability suummary. dips at dip steps.
uuriablity-uummmar. Prediction spans of prediction_lengee time steps reveal anticipated peaks at steps and
4. Designed for cenerationfforecusting, the dataset name dataset offers frequency intervals of descrriitinition over a timeline from start date to end_date Prediction windows of prediction Jength steps enable users to observe patterns such as peak values at peak_stepp and dips around steps. Ranges from min_value to max value suggest considerable variability, with a mean of mean mean_value and standard deviation of std_yalue, illustrating variability -sumiay across the dataset.
S.The dataset name name dataset, spanning start date to date, provides frequency observations of data description. Its prediction horizon set at prediction length steps. Statistical analysis reveals that the data ranges from low of min value a high of max value, with mean of mean value and a variability of std value. Peaks are observed at peak steps, while dips are noticeable around dip steps. demonstrating variability summary
summary throughou the series.
6. Spanning from start date to date, the dataset nume name dataset includes frequency records of data description. Predictions are made for horizons of prediction length time steps. The series ranges between min value and max. value, averaging mean .vauue with variability marked by a standard deviation of value, Anticipated patterns show higher values near stak_steps and lower ones around dip steps. reflecting variability summary over time.
7. The datoset name dataset includes domain time seriey with frequency frequency, sponning from start stae end date. The time series steps ahead. The dotaset's 's range of values varies between min value and max value, with a mean of mean value and a standard deviation of std value. The series shows notable fluctuations, with potential peaks observed at peak steps and troughs around dip steps.
contain total steps time points, with predictions required for prediction length
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Bootstrapping Text to Control Time-Series Generation
8he datoset_name dataset's subdataset includes domain time series data collected at frequency intervals, spanning from start date end date. The total number of time steps total steps, and the forecast horizon is prediction Jength. Data statistics reveal that the values range from minimum of min_value to a maximum of max_value, with an average of mean_value value and standard deviation of vtd_value, summary. Notable peaks are anticipated at time steps peak_steps, while predicted dips are expected at stepst
indicating variabiliry aariablity-summmry.
The subdataset from the dataset name dataset contains domain time series recorded at frequency intervals, ranging from start date to end date. The series consist of total steps time points, and the forecast length is prediction length. The time series data spans a min_value value to max_value, with an average of mean_valus value and standard deviation of value The data exhibits variability summary, with potential peaks at peak steps and troughs at dip steps.
range of values from min,
A.8. Using LLM directly for Time Series Forecasting
We first explored whether rule-based text could work here as it sample and easy to implement. But its results show fluctuations within a certain range, which confuses the model.
Example rule-based template like "The air passengers dataset provides monthly totals of US airline passengers from 1949 to 1960. The data can be described as small consolidation small consolidation small consolidation small consolidation small breakout rebound small consolidation small rounded bottom small consolidation small downtrend small consolidation small false _brakkou rebound small breakout small downtrend slight breakout small downirend rebound small breakout moderate downtrend small breakout moderate downtrend small false breakout small breakout moderate downtrend pullback small breakout small breakkuu small double bottom small breakout significant downtrend."
We further leverage LLM to optimize it, aiming to make it more narrative-like For example
The input time series illustrates market with frequent minor fluctuations and distinct pattern formations. It begins with a small consolidation, followed by a false breakout and a rounded bottom. After another phase of consolidation, the market experiences a slight rebound, then consolidates again before another rebound. This is followed by small breakout and the formation of double bottom pattern. A rounded bottom and descending triangle emerge, succeeded by a pullback. The market then forms another double boltom and rounded bottom, followed by a period of consolidation. Two more rounded bottoms appear before a small breakout and a subsequent rebound. The series concludes with a final phase of consolidation and the formation of a double bottom.
The inpul time series illustrates market with frequent minor fluctuations and distinct pattern formations. It begins with a pullback. followed by a small false breakout and a rebound. After a brief period of consolidation, the market experiences another pullback and forms a small rounded bottom. This is followed by another phase of consolidation and second small rounded bottom, leading to a rebound. The market forms a small rectangle pattern, rebounds again, and creates a small wedge before pulling back once more. Another small rounded bottom forms,followed followed by a rebound and two consecutive small false breakouts. The series then undergoes another pullback, experiences a small breakout, forms a small rectangle, and pulls back again. Finally, the market rebounds and concludes with a small breakout.
Directly employed In-context learning (ICL) to activate LLMs for text generation is also considered. In this setup, the time series first adopts Seasonal-Trest decomposition using Loess (STL) (Cleveland et al., 1990), which is a robust method to decompose time series into long-term trend, seasonal, and residual components. Then, descriptions are generated separately for the initial intermediate, final, and overall trends. It is important to note that this textual description is based on periodicity rather than time, as the time series is more nuanced. Descriptions segmented by time showed erroneous outputs in experiments, particularly in the form of regular fluctuations within specific intervals. For detailed prompt design consult. Example like:
** Initial Phase (roughly first 30-35 points)*s; The series starts with relatively low values, mostly fluctuating between 4 and 6. There's 's a slight downward trend initially, followed by a period of relative stability. A few short-lived spikes occur (e.g.. around points 7-8), but quickly return to the baseline.
Transittion Phase (around points 35-40)*#: There's 's a sudden and significant level shift upwards. Values jump from the 4-6 range to the 8-10 range. This marks the most dramatic change in the entire series.
** **Middle Phase (roughly points 40-70)**: The series settles into a new, higher range, mostly between 9 and 11, There's 's increased volatility compared the initial phase. A slight upward trend is visible within this phase. Several cycles of rises and falls occur, but each cycle tends to peak higher than the last.
**Late Phase (final 30-35 **; The upward trend becomes more pronounced. Volatility increases furressssfurther with larger swings between highs and lows. The series reaches its highest points in this phuse, with peaks above 12. Despite the higher
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Bootstrapping Text to Control Time-Series Generation
Original Time Series
10
01
02
04
2021
Jan
Trend
10.0
5.0
01
02
03
04
2021
Jan
Seasonal
04
2021
Residual
01
04
15n
Figure 4: STL Visualization
peaks, there are still significant drops, creating a saw-tooth pattern.
**veral * #; The series shows clear overall upward trend from start to finish. This trend is not linear but rather step-like, with major shift in the middle and then more gradual increase. The trend is accompanied by increasing volatility over time.
A.9. Evaluation
To further ensure the neutrality and safety of the text templates, conducted the verifications by both automatic and human evaluation: We used GPT-4 to assess whether the templates could be linked to specific TS. Results confirmed that no such linkage existed. Independent human reviewers examined the templates and concluded that they did not reveal any specific details about the TS.
B. Diffusion-based Time Series Generation
B.1. Semantic Prototype Assignment and Alignment
Semantic Prototype Assignment. Although the same set of prototypes is used across different instances, the degree to which each prototype explains different instances varies. To address this, assign prototypes to each time series and text description pair, which serves as a condition for the generation model. For each input sequence Jr (comprising both the time series and text embeddings), a weight vector is generated, the dimension of which corresponds to the number of prototypes. This is achieved via a feature extractor Φ. Each element of the vector ((x); reflects the contribution of each prototype unit p; in the prototype set P, and these weights modify the attention mechanism used during generation. As a result, the model is conditioned on the assigned weighted prototypes. The weights are applied through an attention mask m, which operates on the attention weights for prototypes. To ensure sparsity, we discard prototype units that are assigned with negative weights by setting their attention weights zero. Formally, the prototype assignments transformed into attention mask as follows:
m Φ(20, to) Io( 00
where o0I, t) E Nx is the output from the feature extraction layer that processes both time series and text embeddings. To(a.))so is an indicator function that zeroes out negative weights, ensuring that only retains non- non-negative values. In this work, we use randomly orthogonal vectors as prototypes (Huang et al., 2025). In future work, we will consider methods such as fine-tuning.
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Bootstrapping Text to Control Time-Series Generation
Algorithm 1 Bridde Training
Require: Time series-text dataset {X, }}
Ensure: Network parameters & and 0, prototypes P
1: Initialize prototypes P
2: repeat
3: Sample 20, from 1
4: Extract prototype assignments m = ((r,, P)
5: Randomly set P as unconditional identifier pre
6: Randomly sample time step n ~ (1(1, N)
7: Randomly sample noise (0, I)
8:
Encode text description: I TextEncoder( Corrupt data 2n = vo + v1 O'f Predict step noise with ? = E (I,,, n, m,
9:
10:
11:
Compute loss and take gradient step
12:
until maximum training step
Output: Network parameters Φ, and prototypes P
Algorithm 2 Bridge Inference
Require:
Prototypes P, time series prompts x from train set or few-shot demonstration set, and text description from test
set
Ensure: Generated time series samples
1: Extract prototype prompts m Φxx; P)
TextEncoder(s) 3: Randomly sample noise N ~ 10
Encode text description
4: for from N to do
5: Predict step noise with én ( m,1))
6: Denoise h 1l--1-aa- an
Van
7: end for
8: & = 20
9: Output: Generated samples f
Semantic Prototype Alignment. To condition the denoising diffusion process, we adapt the denoising objective using as a condition, influencing the model's intermediate layers through cross-attention. This ensures that the generated time series aligns with the specified conditional instruction. To achieve this, we aim to align the condition and semantic prototypes during the training phase. set number of query embeddings are allocated to both the text and time series as input. These queries interact with each semantic prototype through cross-attention layers (inserted every other transformer block 2). We initialise the weights of the cross cross-attention layers randomly and update them during training. Specifically, apply cross-attention to the feature representations using the following equations:
Q = WQ ez,
K= K=Wk- W=KK-P, P, V =
(3)
where
Q(K) Vd
= FF
softmax
+ .V)
(4)
Here, E R. xd denotes the output from the attention block. Wo, WK. Wv E gdxd are learnalee projection matrices applied on the sequence dimension. The attention output 2jinal passed to another feedforward network to produce the final output (.
B.2. Algorithm for Generation
Algorithm and Algorithm shown the diffusion process of generate new sample with text guide.
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Bootstrapping Text to Control Time-Series Generation
B.3. The form of Input and Output
Input of Diffusion Model including a time series and corresponding text description. The two of them will be processed by the encoder and LLama respectively, and the obtained embedding will be fused through single-layer MLP as conditional input. The output of the diffusion model is synthetic time series.
C. Baseline Model
C.1. Time Series Generation Model
TiAeLQV et al., 2023) is generative model designed for sequential data. It combines the strengths of a variational autoencoder (VAE) with vector quantisation to discretise latent space representations, making it effective for time series data. The model consists of an encoder that compresses the input data into a discrete latent space and a decoder that reconstructs the time series. TimeVQVAE is particularly useful for generating realistic time series samples while maintaining key temporal dependencies. The quantisation step helps in learning discrete representations that can be reused for efficient time series modelling and generation.
TimeGAN (Yoon et al., 2019) is a variant of the GAN framework specifically tailored for time series data. It combines both supervised and unsupervised learning approaches, using a generator to create synthetic time series and a discriminator to differentiate between real and generated data. Additionally, it integrates an embedding network to capture temporal dependencies and preserve temporal correlations between generated samples. The model ensures that the generated time series not only closely mimic the statistical properties of the original data but also maintain the correct temporal ordering and dynamics. TimegGAN is particularly useful in applications requiring realistic synthetic data generation, such as forecasting and anomaly detection.
GT-GAN (Jeon et al., 2022) introduces a novel architecture for time series generation by incorporating both global and local perspectives. The model features two generators: one focuses on capturing the global trends across the entire time series, while the other focuses on local variations. The two components work together to ensure that the generated time series exhibit realistic patterns both macro and miero levels. GT-GAN uses two-stream discriminator that evaluates both the global and local outputs, ensuring high fidelity in the generated data. This model is effective for generating complex time series where both long-term trends and short-term fluctuations are important.
Time VAE (Desai et al., 2021) extends the traditional VAE architecture to model time series data. It uses an encoder to map time series data into a continuous latent space, from which the decoder reconstructs the original time series. The model captures uncertainty and variation in the data through the latent space's s probabilistic structure, making well-suited for applications where capturing latent factors and generating multiple plausible future scenarios is important. TimeVAB can be applied to various tasks, such anomaly detection, forecasting, and data augmentation, by learning complex temporal dependencies and generating realistic time series that adhere to the original data's 's statistical properties.
C.2. Time Series Forecasting Model
Time-LLM (Jin et al., 2023) is powerful TS LLM that outperforms specialized forecasting models, which repurposes LLMs for time series forecasting by reprogramming input data and employing the Prompt-as-Prefix (PaP) technique for enhanced context alignment.
GPT4TS (Zhou et al., 2023a) takes advantage of pre-trained language and vision models for general time series analysis. By demonstrating that supervised fine-tuning (SFT) can successfully extend LLM capabilities to time series tasks, GPT4TS bridges the gap between natural language processing models and temporal data analysis. The model's 's architecture shows the feasibility of applying large pre-trained models to time series, leading to significant performance improvements in various time series applications.
LLM4TS (Chang et al., 2023) is an innovative framework that repurposes pre-trained LLMs for time-series forecasting, employing two-stage fine-tuning strategy and a two-level aggregation method to align with and enhance the model's 's ability to process multi-scale temporal data, outperforning state-of of-t--of-the-ar models in both fune-tuing and few-shot scenarios.
TEMPO (Cao et al., 2024) proposed using prompts to adapt to different time series distributions. demonstrates superior performance in zero-shot settings across diverse benchmark datasets, showcasing its potential foundational model- building framework for capturing dynamic temporal phenomena.
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D. Experiment Setup
The time series length T for generation is set to 168 a form of non-overlap sequence slices for all the datasets. For forecasting, we assessed performance over four different prediction horizons H E {24, 36, 48, 60) for ILI and H E 48) for M4. The dataset is divided into training, validation, and test sets in a ratio of 8:1:1. All models are tested with the same dataset size, as adding text descriptions to time series sliding is resource- -iotureeintenssve, prompting reduction in the number of sliding windows.
E. Implementation Detail
We implemented all the model and conduct experiments on single NVIDIA Tesla A100 80GB GPUs. For LLLM used in proposed model is LLama3-8B (Dubey et al., 2024). For generation task, we keep all model's sequence length is 168 which the max length of Pedest an, Rain, Temperature datasets. For evaluation of the synthesis data quality task, keep the sequence length of 256. The dataset is divided into training, validation, and test sets in ratio of 8:1:1.
The reported result are all under following training settings. The number of prototypes are set to 16 for all the main evaluations. Models for each sequence length are trained for 50, 000 steps using a batch size of 128 and a learning rate of 5 10 5 with 1, 000 up steps.
F. Dataset Analysis
F.I Details of Datasets
In this section, we provide a detailed overview of the datasets used for model training in this paper: Electricity: This dataset represents the hourly electricity consumption of 321 clients from 2012 to 2014, measured in kilowatts (kW). It was originally extracted from the UCI repository.
Solar: This dataset contains 137 time series representing hourly solar power production in the state of Alabama throughout 2006.
Wind:
This dataset contains single extensive daily time series representing wind power production (in megawatts) recorded at 4-second intervals starting from August 1, 2019. It was downloaded from the Australian Energy Market Operator (AEMO) online platform.
Traffic: This dataset contains 15 months of daily data (440 daily records) describing the occupancy rate (between 0 and 1) of different car lanes on San Francisco Bay Area freeways over time.
Taxi: This dataset contains spatio-temports traffic time series of New York City taxi rides recorded 1,214 locations every 30 minutes during January 2015 and January 2016.
This dataset contains hourly pedestrian counts captured from 66 sensors in Melbourne starting from May 2009, The original dataset is regularly updated when new observations become available. The dataset used here contains pedestrian counts up to April 30, 2020.
Pedestrian:
Air Quality: This dataset was used in the KDD Cup 2018 forecasting competition. It contains hourly air quality measure- ments from 59 stations in two cities: Beijing (35 stations) and London (24 stations) from January 1, 2017,, to March 31, 2018. The air quality measurements include various metrics such as PM2.5, PM10, NO2, CO, 03, and SO2. Missing values were imputed using leading zeros or the Last Observation Carried Forward (LOCF) method.
Temperature:
This dataset contains 32,072 daily time series with temperature observations and rain forecasts gathered by the Australian Bureau of Meteorology from 422 weather stations across Australia between May 2, 2015, and April 26, 2017. Missing values were replaced with zeros, and the mean temperature column was extracted for use.
Rain: This dataset focuses on rain data extracted from the same source as the Temperature dataset.
This dataset was used in the NN5 forecasting competition. It contains 111 time series from the banking domain with the goal of predicting daily cash withdrawals from ATMs in the UK. Missing values were replaced by the median across all corresponding days of the week throughout the entire series.
NN5:
Fred-MD: This dataset contains 107 monthly time series reflecting various macroeconomic indicators sourced from the
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Domain
Tasks
Datasets
Dim.
Series Length
Dataset Size
Frequency
Long-Term
ILI
7
24, 36, 48, 60
(617. 74, 170)
I week
Illness
M4-Yearly
(23000, 0, 23000)
Yearly
Demographic
M4. Qu-artrtrryy
(24000, 0, 24000)
Quarterly
Finance
Short-term
M4-Momblyy
(48000, 0, 48000)
Monthly
Industry
Forecasting
M4-Weckly
(359. 3599)
Weekly
Macro
M4-Daily
14
(4227, 0, 4227)
Daily
Miero
M4 M4- Hourly
48
(414, 0, 414)
Hourly
Other
Table 7: Comparison of datasets for long-term and short-term forecasting tasks
Federal Reserve Bank's FRED-MD database. The series have been differenced and log-transformed following established practices in the literature.
Exchange: This dataset records daily exchange rates for eight currencies.
Stock: This dataset consists of daily stock prices for the symbol GOOG, which is listed on NASDAQ.
Web: This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145,063 daily time series representing the number of hits or web traffic for Wikipedia pages from July I, 2015, to September 10, 2017. Missing values were replaced with zeros.
F.2 Dataset Statistics
To test the quality of the synthetic data generated by our proposed model, we conducted tests on two additional datasets. In the experiments, we trained the synthetic data be the same as the original data and tested it the real datasets. The statistics of the datasets in Table 7:
G. Evaluation Metrics
The calculations of these metrics are as follows:
H Σ
H Σ
MAE 1
MSE
(Yn
1 - 8xh
H
H h=1
h=1
200
"1
1Y, Ynl
100 H
Yn
Yml
SMAPE
MAPE
H
VAl + [VA
k=1
h=1
MASE 1 H
Σ
Yn YEl
OWA 1
SMAPE
MASE
D
j Yg Yj
2
MASExave2
where is the periodicity of the time series data, H denotes the number of data points (i.e., prediction horizon in our cases), and Y, and Y,, are the h-th ground truth and prediction, where E (1....,
For generation, we consider Marginal Distribution Difference (MDD):
MDD(P, Q) = Σ P(xx) Q(x)
#EX
where P and Q represent the marginal distributions of the real and synthetic data, and X denotes the set of possible values for the variable being analyzed.
Also Kullback-Leibler divergence (K-I.)
P(x)
(PI() = Σ P(x) log
Q(x))
xEX
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Random
LLMHTS
TEMPO
Time-LLM
GPTTTS
Dotaset
Bridge
1.98
0.89
1.21
1,02
2.20
1.44
2.19
1,02
KerneiSyndh
1.35
1,30
1.61
1.07
1.43
1.01
3,80
1.42
MASE
OWA
OWA
SMAPE
MASE
OWA
SMAPE
OWA
Bridge
13,10
1.99
0.93
12.25
1,73
0.87
12.85
1.90
0.96
13,10
1.99
0.93
M4
Reaf
26.46
4.43
1.92
12.33
1.80
0.88
12.09
1.72
0.85
12.50
1.77
0.89
12.70
1.94
0.91
KemnelSynch
14.39
2,09
1.02
13.94
2,00
0.99
12.594
1,735
0.918
14,56
2,08
1,02
Table 8: Comparison of MSE and MAE for TS forecasting across methods, with results for four forecasting horizons: H {24, 36, 48, ) for ILI and H E {6,48) 48} for M4. Average results are reported.
where and Q are the two probability distributions being compared, and X represents the set of possible values.
H. Human Evaluation
In addition to the quantitative metrics, we conducted a human evaluation to assess the quality of the generated time series descriptions.
H.1. Evaluation Process:
The human evaluation involved team of trained annotators who were tasked with reviewing the generated time series in relation to the given text descriptions and ranking the candidate. The process was organized into two primary stages:
Preliminary Setup Annotators were presented with a single text description accompanied by a set of time series. Each set was evaluated independently, with annotators assessing how well each time series captured the trends, patterns, and anomalies described the corresponding text. We define two evaluation settings: (i) HE-Rank, annotators ranked the time series generated by different model settings together with the ground truth, focusing purely relevance. (ii) HE-Mixed, the same sequences were evaluated alongside three distractor time series randomly sampled from the test set, forming a mixed pool of candidates for relative ranking.
Evaluation Criteria To help annotators better understand and standardize the evaluation process, we provide a set of reference dimensions.
Relevance to given text descriptions: Whether the time series description accurately reflects the patterns, trends, and anomalies observed in the text.
Semantic Alignment: Whether the time series properly conveys the underlying meaning of the texr, such identifying upward and downward trends, spikes, and seasonal behaviors.
Plausibility: Whether the time series makes sense within the domain context (e.g., finance, healthcare), and offers a reasonable interpretation of the data.
Coherence: Whether the time series is logically consistent within itself, avoiding contradictions and ensuring that the time series aligns with the observed trends or patterns throughout the text.
Reliability of Evaluation To ensure consistency and minimize evaluator bias, multiple annotators assessed each time series. The final score for each description was calculated as the average score from all annotators. Annotators were trained on a common set of guidelines to ensure that the evaluation criteria were applied consistently across all descriptions.
Performance of downstream tasks
Table 8 shows the quality of generated data for the purposes of training models for downstream tasks. We generated synthetic data on two additional datasets to assist existing SOTA models in TS forecasting. All models were trained either using only real data or synthetic data and then tested on real test sets. The results indicate that training with only synthetic data can achieve comparable performance to real data across all models, as performance differences between real and synthetic data are less visible than differences in performance between architectures. This suggests that the generated data
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is sufficiently realistic, potentially allowing share synthesised surrogates of otherwise sensitive data. For comparison, we also employed KernelSynth (Ansari et al., 2024) methods. Both methods effectively provided valuable synthetie data (compared to completely random data), but our proposed approach produced data that more closely resembles real data. This underscores its potential for generating meaningful synthetic data across domains.
J. Ablation Experiment on the Influence of Text Types on Diffusion Models
Table 9 show the performance of different text types. Overall, the inclusion of text descriptions in time series modeling demonstrates consistent benefits across most datasets, with specifie nuances depending on the approach and dataset characteristics. Across most datasets, the inclusion of textual descriptions significantly improves alignment metrics, with the Bridge (w/o Background) approach often demonstrating superior performance compared to both Bridge (w/o Sattern+Statistic) and Bridge (w/o Text).
For MDD, Bridge (w/o Background) consistently outperforms or matches other methods in several datasets. For instance, in the "Rain" dataset, Bridge (w/o Background) achieves the lowest score (5.477), outperforming Bridge (wo Stttern+Statistic)) (5.499) and Bridge (w/o Text) (6.002). Similarly, in the "Pedestrian" dataset, Bridge (w/o Background) achieves the best score (0.560), indicating its ability to capture intricate patterns better than the other methods. While Bridge (w/o Sattern+Statiisic) occasionally achieves slightly better results (e.g., in the "Electricity" dataset with 0.110 compared to 0.139 for Bridge (w/o Background)), Bridge (w/ Background) demonstrates more consistent performance across diverse domains.
The K-L Divergence results further highlight the strengths of Bridge (w/o Background). In the "Wind" dataset, for example, Bridge (w/o Background) achieves the lowest divergence (0.048), outperforming both Bridge (wo StateintStatistc)) (0.052) and Bridge (w/o Text) (0.056). Similar trends are observed in the "NN5" and "Temperature" datasets, where Bridge (w/o Background) consistently produces the lowest K-L scores, indicating its capability effectively encode temporal patterns with textual assistance. Notably, in datasets like "Air", Bridge (w/o Background) achieves significant improvements compared to Bridge (w/o Text), further emphasizing its advantage in handling complex and noisy time series data.
K. The Impact of Prototype
The results in Table 10 demonstrate the influence of prototype quantity on marginal distribution distance and K-L divergence across multiple datasets. Increasing the number of prototypes generally leads improved performance, as observed in the Electricity dataset, where the marginal distribution distance decreases from 0.615 (4 prototypes) to 0.135 (16 prototypes). Similar trends are evident in datasets such as Traffic (1.211 to 0.315) and NN5 (1.550 to 0.613).
For K-L divergence, a higher number of prototypes often results in lower divergence values, indicating better alignment with the target distribution. For example, in the Taxi dataset, K-L divergence drops from 0.154 (4 prototypes) to 0.003 (16 prototypes). However, certain datasets, such as Wind and Exchange, exhibit less consistent trends, suggesting potential variations in data characteristics affecting prototype effectiveness.
L. The Impact of LLms on the diffusion model performance
The Table 11 compares the performance of Illama and GPT2 as encoders in our diffusion model across various time series domains. Both models show similar performance in most domains, with slight differences in specific cases. For example, Llama performs slightly better in the "Electricity" (0.139 vs 0.174) and "NN5" (0.570 vs 0.887) domains, suggesting a better ability to capture fluctuations in these time series. In contrast, GPT2 outperforms Llama in "Pedestrian" (0.578 vs 0.483) and "Fred-MD" (0.226 vs 0.225), indicating its strength in encoding gradual trends. Overall, both models show strong performance across multiple domains, with only minor variations. These results highlight that while Llama and GPT2 differ slightly in their handling of specific time series patterns, both effective encoders for our diffusion model, capable of capturing both domain-specific and general temporal features.
M. Data Augmentation Results
For long-term forecasting (Table 12), we find that the LLM4TS trained via the synthetic data produces relatively low MSE and MAE values, such as ILI-24 Synthesis with an MSE of 1.84 and an MAE of 0.85, which are competitive with the performance real-world datasets. In fact, for length like 24 and 36, LLM4TS consistently performs well, showing
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Dataset
w/o Sattern+Statistic
w/o Background
w/o Text
Rule-based
Electricity
0.110
0.139
0.135
0.256
Marginal Distribution Distance
Solar
375.531
375.530
375.531
377.232
Wind
0.344
0.316
0.304
0.422
Traffic
0.324
0.309
0.315
1.178
Taxi
0.328
0.325
0.338
0.641
Pedestrian
0.584
0.560
0.576
1.277
Air
0.472
0.440
0.418
0.665
Temperature
0.332
0.331
0.356
0.572
Rain
5.499
5.477
6.002
9.533
NNS
0.591
0.570
0.613
1.377
Fred-MD
0.239
0.226
0.228
0.460
Exchange
0.315
0.316
0.376
0.430
Electricity
0.002
0.003
0.001
0.008
Solar
0.008
0.006
0.005
0.033
Wind
0.052
0.048
0.056
0.144
Traffic
0.013
0.012
0.016
0.027
K-L Divergence
Taxi
0.020
0.014
0.020
0.093
Pedestrian
0.006
0.009
0.009
0.079
Air
0.006
0.005
0.021
0.042
Temperature
0.023
0.016
0.020
0.904
Rain
0.006
0.006
0.008
0.016
NN5
0.008
0.004
0.004
0.101
Fred-MD
0.005
0.004
0.023
0,108
Exchange
0.062
0.057
0.067
0.350
Table The performance different text type. Marginal distribution distance scores (MDD) and K-I. divergence (K-I.) are reported.
Prototypes
4
8
16
32
64
4
8
16
32
64
Electricity
0.615
0.368
0.135
0.117
0.236
0.006
0.027
0.001
0.001
0.020
Marginal Distribution Distance
Solar
375.536
375.531
375.531
375.532
375.556
0.025
0.016
0.005
0.008
0.017
Wind
0.271
0.299
0.304
0.314
0.309
0.059
0.074
0.056
0.075
0.093
Traffic
1.211
0.287
0.315
0.349
0.411
0.200
0.018
0.021
0.022
0.027
K-L Divergence
Taxi
1.008
0.433
0.338
0.371
0.384
0.154
0.013
0.003
0.020
0.019
Pedestrian
1.599
0.921
0.576
0.554
0.532
0.075
0.022
0.009
0.003
0.002
Air
0.611
0.393
0.418
0.515
0.544
0.010
0.009
0.005
0.006
0.009
Temperature
0.487
0.317
0.356
0.330
0.307
0.113
0.025
0.020
0.017
0.042
Rain
5.763
4.981
6.002
5.548
6.420
0.014
0.009
0.010
0.008
0.018
NN5
1.550
0.796
0.613
0.616
0.573
0.130
0.118
0.004
0.005
0.009
Fred-MDD
0.407
0.241
0.228
0.245
0.346
0.012
0.015
0.006
0.009
0.030
Exchange
0.365
0.359
0.376
0.309
0.330
0.062
0.063
0.067
0.083
0.046
Table 10: Ablation experiment on the impact of the number of prototypes.
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Model
Electricity
Solar
Wind
Traffic
Taxi
Pedestrian
Llama
0.139
375.531 375.538
0.316
0.309
0.325
0.578
GPT2
0.174
0.325
0.331
0.361
0.483
Air
Temperature
Rain
NN5
Fred-MD
Exchange
Llama GPT2
0.440 0.645
0.331 0.349
5.932
0.570
0.226 0.225
0.374
5.994
0.887
0.414
Table 11: Model performance across different domains. Result measured by MDD
competitive results in both MSE and MAE, even when compared to training real data. GPT4TS and Time-ILLM, on the other hand, exhibit a slight drop in performance when trained on synthetic data, but considerable accepted. In the short-term forecasting scenario (Table 13), the results show similar trends. This suggests that synthetic data can effectively simulate real data patterns, making a viable option for model training when real-world data limited or unavailable.
We also compare the quality of our synthetic data generation with the Kernel Synth method employed by Chronos (Ansari et al., 2024). While the long-term ILI forecasting task models trained on data clearly outperform models trained on KernelSynth, the picture is slightly more nuanced for shorty-term M4 forecasting. Since the forecast horizons are shorter in M4, the overall difference is less nuanced. Table 14 details for which sub-datasets and models trained on our data are performing better and for which sub-datasets and models KernelSynth is more suitable. These comparisons are further contextualised by statistical analysis for SMAPE and MASE metrics, we conduct t-test on the individual scores directly. For OWA, since it involves averages of SMAPE and MASE, we use the bootstrapping technique by sampling 1000 times with replacement to obtain the distributions of scores and conduct the t-test on this subset. Overall our method performs better on half model/subset combinations, of which all but results are statistically significant.
Methods
LLM4TS
TEMPO
Time-LLM
GPT4TS
Metrics
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
ILI-24 KernelSynth
4.36
1.49
1.48
1.02
1.21
0.93
3.92
1.45
ILI-36 KernelSynth
4.32
1.49
1.37
0.96
1.31
0.94
3.87
1.43
ILI-48 KernelSynth
4.15
1.48
1.69
1.09
1.49
1.04
3.77
1.40
IL1-60 KernelSynth
4.35
1.50
2.01
1.22
1.71
1.11
3.62
1.39
ILI-24 Ours
1,84
0.85
1.00
0.87
2.05
1.29
2.23
0.99
ILI-36 Ours
1.86
0.86
1.22
0.99
2.13
1.34
2.13
0.97
ILI-48 Ours
1.88
0.88
1.34
1.08
2.35
1.60
2.28
1.05
ILI-60 Ours
2.37
0.99
1.49
1.14
2.30
1.55
2.35
1.09
ILI-24 Real
1.78
0.81
0.66
0.63
1.83
1.15
1.99
0.88
ILI-36 Real
1.75
0.82
0.92
0.80
1.90
1.17
1.90
0.90
ILI-48 Real
1.72
0.84
1.33
1.02
2.16
1.26
1.81
0.88
ILI-60 Real
2.20
0.95
0.91
0.80
2.11
1.23
1.87
0.92
Table 12: Comparison of MSE and MAE across various methods Long-term forecasting. The results for four different forecasting horizons: H {24, 36, 48, 60). Red values indicate the best score, and blue values represent the second best.
N. Prototypes Sample Result
Figure shows 16 semantic prototypes used in TSG models. Each prototype represents a distinct pattern in time series data, enabling the generation of diverse, domain-specifi series. For example, prototypes (0,6,15) capture cyclical patterns useful for seasonal trends. Prototypes {3,12} represent trend patterns, including gradual changes and sharp transitions. Prototypes [1,2,7] show high-frequency fluctuations, representing volatility. By combining these prototypes, the model can generate rich, domain-specific time series data through translating text into time series data with specific semantic concepts. Figure Figure 17 shows the distribution of prototypes across various domains. Some prototypes, like Prototype 0 in
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KemeeSmok
Kemolljam
Monki
1017
Jverage
12.253
13,004
Tearly
Average
Table 13: Time series forecasting results unseen time series dataset. The forecasting horizons are in [6, 48] and report value is the average. A lower value indicates better performance. Red: the best, Blue: the second best.
LLM4TS
TEMPO
GPT4TS
Frequency
SMAPE
MASE
OWA
SMAPE
MASE
OWA
SMAPE
MASE
OWA
Monthly
True
True
True
True
True
True
True
True
True
Quarterly
True
True
True
True
True
True
True
True
True
Yearly
True
False
True
True
True
True
True
False
True
Table 14: Statistical significance results (p 0.05) for differences in SMAPE, MASE and OWA metries between model
trained our and KernelSynth data based on scores reported in Table 13. Results where method is better are highlighted in bold.
"temperature" and "electricity," are widely relevant, while others, like Prototype 15 in "traffic," are domain specific. The sparsity of the heatmaps shows that not all prototypes are equally important within a domain. For example, "solar" primarily uses prototypes 10,3,. This demonstrates the flexibility the prototype-based approach, capturing both general and domain-specific patterns.
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Prototype
Prototype
Prototype 2
Prototype 3
1.05
0.5
0.5
1.00
0.0
0.0
0.95
00.5
-0.5
0.90
-1
11.0
100
100
100
Prototype
Prototype
Prototype
Prototype
1.0
0099
0.95
0.5
1.00
-0.5
-0.5
0.0
-1.05
100
100
100
100
Prototype
Prototype
Prototype 10
Prototype 11
1.0
0.5
0.5
0.5
0.0
0.0
0.0
0.0
-0.5
0.5
0.2
-0.5
-1.0
100
100
100
100
Prototype 12
Prototype 13
Prototype 14
Prototype 15
$ VOM
0.5
0.5
0.0
0.0
-0.5
-0.5
-1
100
100
100
Figure 5: Visualization of semantic prototypes. Each prototype represents different pattern or characteristic commonly found in time series data.
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Mask Heatmap electricity
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Figure 6: Prototype distribution for the electricity domain. Each heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Figure 7: Prototype distribution for the solar domain. Each heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Figure 8: Prototype distribution for the wind (4 seconds) domain. Each heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Figure 9: Prototype distribution for the traffic domain. Each heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Bootstrapping Text to Control Time-Series Generation
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Figure 10: Prototype distribution for the taxi domain. Each heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Figure 11: Prototype distribution for the pedestrian domain, Each heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Figure 12: Prototype distribution for the kddcup domain. Each heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Figure 13: Prototype distribution for the temperature domain. Each heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Figure 14: Prototype distribution for the rain domain. Each heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Figure 15: Prototype distribution for the nn5 domain. Each heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Figure 16: Prototype distribution for the fred_md domain. Bach heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Figure 17: Prototype distribution for the exchange domain. Each heatmap shows prototype indices (x-axis, 0-15) and their frequency importance (color intensity).
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Reviews:
[{"review_id": "BeUmonAa4c", "reviewer": "Reviewer_QJJi", "summary": "The paper considers how to train a generator for time series (the time series generation problem). A multi-step system (process) is created, that includes LLM prompts, time series features and specification, and training.  There is also a two-team multi-agent method embedded for some optimization within the system.  Several time series data are used, and numerical comparisons are made to some other methods. The primary contribution is some generalization across different time series training data. The numerical results are somewhat mixed in various measures of success.\n\n## update after rebuttal\n\nThe authors have provided useful explantation and discussion of their work.  The overall concept and approach is interesting, and I have raised my score.  However, I am still skeptical that the time series application is a good one for this work, and believe that a good generator can be trained without using the semantic approach.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the reviewer's time and feedback, especially the high-level perspective on the entire system, which will guide the future expansion of our work. Thank you!\n\n####  **General Comments (GC):**\n\n**[GC 1]** *Claims of generality are not very clear.* *\"The time series specifications (or features) are hand-crafted, and a rich enough set of these is selected.\"*\n\nSection 4.1 generates text templates, which are then applied to TS data, with **an LLM filling in general, domain-agnostic statistical features** to produce the final text. Thus, **time series specifications are automatically generated by the LLM based on templates** from the multi-agent system, **rather than being manually crafted or selected by human intervention**. We will make it more clear in future revisions.\n\n**[GC 2]** *\"The extent and impact of human interventions are unclear.\"*\n\nThere are only two points in our work where human actions are involved:\n\n1. After collecting the initial text templates **(Section 4.1 Step1)**, LLM prompting filters them to ensure no dataset-specific information is included. A human double-checks the results to prevent potential data leakage.\n2. A human assessment (Section 6.2) serves only as an evaluation metric and does not affect model training.\n\n**[GC 3]** *\"It isn't clear that existing time series signal decomposition and feature extraction methods couldn't be applied to this data and used for training a generator.\"*\n\nWe tried rule-based text generation and Seasonal-Trend Decomposition, but both methods were ineffective in generating useful descriptions (**the first paragraph in Section 4 and Appendix A9**).\n\n**[GC 4]** *\"The contribution to time series analysis and generation is very small.\"*\n\nOur framework is designed specifically to addresses key challenges in text-controlled TS generation, such as lack of aligned data, controllable generation, and domain generalization. We integrate multi-agent reasoning for data construction, semantic prototype conditioning, and a diffusion-based generator to address challenges.\n\nWithin the multi agent system, we propose tailored evaluation criteria like trend description accuracy, seasonality, completeness, and clarity (Appendix A.5) to assess TS generation. We also explore the impact of different text types on generation, showing that pattern descriptions and concise summaries are more effective (Section 6.3). These insights offer valuable takeaways for time series tasks.\n\n####  **Other Comments (OC):**\n\n**[OC 1]** *\"It would be very helpful to clearly show the hand-tuned and human interaction portions.\"*\n\nWe clarify that there is no hand-tuning or human intervention for time series features.\n\nPlease refer to our response to **[GC 1]** and **[GC 2]**. \n\n**[OC 2]** *\"Can this framework be applied to other applications?\"*\n\nGreat suggestion! The modular structure could support future applications beyond TS Generation, including in image and video. This is an exciting direction for future work.\n\n**[OC 3]** *“Why is the multi-agent approach a good one? Isn't this just an optimization problem? What other optimizers could be applied?”*\n\nWe chose a multi-agent system over a single-agent or LLM prompt approach for key reasons. Direct LLM prompting was ineffective in generating useful descriptions (see **[GC 3]**), and single-agent systems often suffer from overconfidence, leading to errors without correction [1]. Multi-agent systems allow agents to critique each other’s outputs, improving reliability and transparency by assigning distinct roles.\n\nOur multi-agent approach goes beyond scalar optimization, involving tasks like searching for TS text online, summarizing templates, generating text, evaluating alignment, and refining for better templates.\n\nWhile  we recognize reinforcement learning could optimize a single LLM, it faces challenges: (1) large optimization space, as RL starts from scratch, while our method refines existing templates; (2) high computational demands due to model training, whereas our agents require no retraining or fine-tuning; and (3) sparse feedback from using final generation performance as a reward, while our agents themselves evaluate text clarity, completeness etc (Appendix A5), improving refinement efficiency.\n\n[1] Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents [[arxiv.org/pdf/2501.00430](https://arxiv.org/pdf/2501.00430)]\n\n#### **Question**:\n\n*“Given the hand crafted selection of time series specifications, then why is the LLM-based approach a good one?”*\n\nPlease kindly refer to our response to **[GC 1], [GC 2]** for the clarification on avoiding human intervention in feature selection and **[OC 3]** for justification of the multi-agent system. As general statistics with simple rule-based texts are ineffective for TSG (see Section 6.3), we use a multi-agent system.\n\nIf any further questions or concerns, we’d be happy to provide more information. Looking forward to your response and continued discussions!", "claims_and_evidence": "Claims of generality are not very clear.  The time series specifications (or features) are hand crafted and a rich enough set of these is selected so as to be sufficient for modeling the various time series data. \n\n\nThere is little or no formal theory or convergence analysis, the results are entirely based on tuning the system to work with the data sets considered.  The extend and impact of the human interventions isn't clear, and this leaves the reader wondering about the overall approach and outcomes.", "methods_and_evaluation_criteria": "The data sets are varied to some extent.  However, it isn't clear that existing time series signal decomposition and feature extraction methods couldn't be applied to this data, and use this for training a generator.   The testing here is against other similar algorithms, and the advance seems limited to being somewhat more generalized over the example set.\n\n\nThe system is so highly tuned with so many pieces that, although the entire approach seems reasonable, the reader can't easily judge the overall contribution or outcomes.  \n\n\nOverall, it simply isn't clear that a semantic interface is a good idea for this problem.  Having said that, the paper is most interesting in terms of the overall system approach, but the contribution to the application (time series analysis and generation) is very small.", "theoretical_claims": "Not applicable.", "experimental_designs_or_analyses": "It is not clear how to evaluate the many aspects of filtering, human interactions, and selected time series features.", "relation_to_broader_scientific_literature": "The contribution to time series analysis and generation is minor.  This is because the features and specifications needed amount to many constraints and properties of the observed time series, and these are hand crafted until the semantic specification of these for an LLM leads to something useful.  So if the point is to show an example of using an LLM with a generator, then the approach is somewhat interesting.  But if the true goal is a time series generator, then adding in an LLM creates more ambiguity than it solves, and alternative methods for training generators should be found.", "essential_references_not_discussed": "The paper is not well linked with (decades of) time series literature.", "other_strengths_and_weaknesses": "Please see comments in other sections.\n\n***Added after rebuttal.***\n\nThe authors have provided useful explantation and discussion of their work.  The overall concept and approach is interesting, and I have raised my score.  However, I am still skeptical that the time series application is a good one for this work, and believe that a good generator can be trained without using the semantic approach.\n\n********************************", "other_comments_or_suggestions": "Would be very good to clearly show the hand tuned and human interaction portions.\n\nCan this framework be applied to other applications?\n\nWhy is the multi-agent approach a good one?  Isn't this just an optimization problem?  What other optimizers could be applied?", "questions_for_authors": "Given the hand crafted selection of time series specifications, then why is the LLM-based approach a good one?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "None"}}, {"review_id": "6l2ZFZ2X60", "reviewer": "Reviewer_svQz", "summary": "This paper introduces BRIDGE, a novel framework for text controlled timeseries generation. It addresses two major challenges: lack of high-quality text-to-time-series datasets and difficulty in aligning textual descriptions with time-series data.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewer’s time and thoughtful feedback. We are grateful for the recognition of the strengths of our work, particularly the “straightforward and well-structured\", \"reasonable\" nature of our approach, as well as the \"novel research direction\" of BRIDGE in bridging text-controlled generation with time-series synthesis. We also appreciate the acknowledgment of our \"innovative\" multi-agent dataset synthesis approach. \n\nYour insights are invaluable in helping us refine our work, and we are truly thankful for the opportunity to clarify and improve our paper. Below, we provide our detailed responses to your comments.\n\n#### **General Comments:**\n\n**It would be better to add computational efficiency and human evaluation of text-to-TS alignment experiments.**\n\nWe appreciate this suggestion! In fact, **we have already included human evaluation of text-to-TS alignment in our original submission**. As shown in Table 4 and 5, we compare different configurations (with and without prototypes and text) across multiple datasets using both quantitative metrics (MSE and MAE) and **human evaluation scores (HE and HE@3)**. A detailed discussion of these evaluations can be found in Section 6.2 and 6.5.\n\nFor computational efficiency, please refer to **[Q 2]**.\n\n#### **Questions:**\n\n**[Q 1] How does the multi-agent generated dataset compare to human-annotated text-TS pairs?**\n\nIn fact, the first step of our multi-agent system (Section 4.1 Step 1) involves collecting articles, news, and reports from the Internet that describe time series data, which **serve as human-annotated references** (**\"Initial Text\" in Table 1**). While these outperform rule-based baselines slightly, they are significantly worse than our multi-agent **Refined Text** (Table 1), suggesting **generic human descriptions are suboptimal for this task**.\n\nTo address this, we implement a multi-agent iterative optimization process that refines textual descriptions by **enhancing key aspects such as trend accuracy, mention of seasonality, completeness of information, and clarity of description** (detailed in Appendix A.5). This process strengthens text-to-time series alignment by optimizing **both quantitative metrics** (e.g., MSE, K-S Test, Wasserstein Distance) and **qualitative evaluations** based on a 5-point Likert scale. As a result, our refined text-TS pairs achieve significantly higher quality for TS Generation task.\n\nFurthermore, in **Section 6.3 Table 1** and **Appendix J Table 12**, we provide a detailed analysis of what constitutes useful textual descriptions, offering key insights into the generation of high-quality text-TS pairs.\n\n**[Q 2] What is the computational cost of BRIDGE relative to prior TS Generation models?**\n\nTo evaluate cost-efficiency, we measured  training time  (s/epochs) and inference time (ms/sample) across multiple baselines on same A100 GPU. BRIDGE achieves a favorable balance between performance and cost among diffusion-based models, maintaining high generation quality and controllability with substantially lower computational cost.\n\n| Model | BRIDGE | Diffusion-TS | TimeGAN | TimeVAE | TimeVQVAE | GT-GAN |\n| --- | --- | --- | --- | --- | --- | --- |\n| Inference | 27.7 | 142.8 | 6.3 | 3.84 | 4.75 | 7.23 |\n| Training | 356.6 | 654.7 | 723.1 | 16.67 | 157.1 | 454.3 |\n\n**[Q 3] Can BRIDGE generalize to unseen domains without fine-tuning? Are there cases where prototype alignment fails?**\n\nOur framework was explicitly designed to promote cross-domain generalization by leveraging semantic prototypes and text conditioning. In our experiments (see Table 3), **BRIDGE was able to generate high-fidelity time series in unseen domains (e.g., Stock, Web) without any fine-tuning**, suggesting that the learned semantic structure and prototype conditioning do generalize beyond the training domains.\n\nWhile we did not observe clear prototype misalignment in these settings, we agree that such cases may be possible, particularly in domains with very different temporal dynamics or semantics. We will explore this further in future work by evaluating on a broader set of domains and investigating potential prototype refinement strategies.\n\n---\n\nThank you again for your thoughtful advice. We will certainly incorporate the discussions in the revised version and include references on prompt optimization and LLM-based dataset generation as per your suggestion.\nPlease feel free to raise any further questions—we greatly appreciate your ongoing input!", "claims_and_evidence": "The paper’s key claims are well-supported by empirical results. The multi-agent dataset synthesis claim is validated by MAE reduction, and semantic prototypes show improved MSE and generalization in ablation studies.\n\nHowever, some areas need further validation:\n1. Dataset quality is not compared to human-annotated text-TS pairs.\n2. Computational efficiency is not analyzed, leaving inference cost unclear.", "methods_and_evaluation_criteria": "The proposed method is straightforward and well-structured, leveraging multi-agent dataset synthesis and diffusion-based generation. The use of semantic prototypes for bridging text and time-series data is reasonable and aligns with the problem setting. \n\nHowever, the framework could be further analyzed for scalability and efficiency, as its computational cost is not explicitly discussed.", "theoretical_claims": "This paper primarily focuses on methodological and empirical contributions rather than formal theoretical analysis.", "experimental_designs_or_analyses": "The experimental design and evaluation in the paper are fairly comprehensive：\n\n1. Datasets. The paper evaluates its method on various datasets, including Electricity, Solar, Wind, Traffic, Taxi, Pedestrian, Air, Temperature, Rain, NN5, Fred-MD, and Exchange for in-domain analysis​. Additionally, Stock and Web datasets were used for unseen domain generalization​.\n\n2. Ablation studies includes assess the impact of text conditioning, prototype usage, and different language models as text encoders​.\n\n3. The paper benchmarks against TimeVQVAE, TimeGAN, GT-GAN, TimeVAE, and diffusion-based methods​.", "relation_to_broader_scientific_literature": "This paper builds on prior work in TS generation, text conditioned generative modeling, and diffusion models.", "essential_references_not_discussed": "The paper provides a solid literature review, but it would benefit from references to prior work on prompt optimization and LLM-based dataset generation to better connect it to existing research.", "other_strengths_and_weaknesses": "**Pros：**\n\nThe paper introduces a novel research direction by bridging text-controlled generation with time-series synthesis, which has significant potential for real-world applications.\n\n\nThe multi-agent dataset synthesis approach is innovative and helps address the lack of high-quality text-TS pairs.\n\n**Cons：**\nIt would be better to add computational efficiency and human evaluation of text-to-TS alignment experiments.", "other_comments_or_suggestions": "See above", "questions_for_authors": "1. How does the quality of the multi-agent generated text-TS dataset compare to human-annotated descriptions?\n\n2. What is the computational cost of BRIDGE compared to existing time-series generation models?\n\n3. Can the model generalize beyond seen domains without fine-tuning? The paper demonstrates cross-domain generalization, but are there cases where semantic prototype alignment fails, requiring domain-specific fine-tuning?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "snDJsZZXWS", "reviewer": "Reviewer_xRLG", "summary": "The paper introduces the BRIDGE framework which consists of two broad components - a paired text-time series dataset generation method and a diffusion-based time series generation method conditioned on textual input. \n\nFor the dataset generation, the paper proposes a very detailed and carefully designed framework that leverages LLMs to parse news, articles, reports, etc., to obtain textual templates that can be filled on an instance or sample basis. The generated text caption is iteratively refined using an evaluator feedback to finally obtain a concise and useful text condition for the time series sample. \n\nFor the time series generative model, the paper builds on top of [1] to effectively combine text conditions with semantic prototypes to accurately generate time series samples.\n\nAddition of textual input increases the generalization capabilities, even for unseen domains, and this is validated empirically in the paper.\n\n[1] TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We truly appreciate the reviewer’s insightful feedback! \n\n**[Q1] Justify the use of metrics.**\n\nWe initially prioritized MDD and KL divergence over PS and DS for their robustness, as they are **not influenced by post-hoc models.** We have now included **PS (MAE reported) and DS** and use **J-FTSD** to complement MSE for controllability evaluation. Our model achieves the **best results across all metrics**.\n\n### PS\n\n|Dataset|BRIDGE|TimeVQVAE|TimeGAN|GT-GAN|TimeVAE|Diffusion-TS|\n|-|-|-|-|-|-|-|\n|Electricity|0.091|0.138|0.143|0.585|0.152|0.146|\n|Solar|0.441|1.082|1.631|1.169|0.692|0.566|\n|Wind|0.174|0.326|0.942|1.182|0.423|0.886|\n|Traffic|0.208|0.390|0.618|0.919|0.311|0.754|\n|Taxi|0.234|0.319|0.339|0.687|0.296|0.556|\n|Pedestrian|0.149|0.456|0.488|0.603|0.359|0.429|\n|Air|0.113|0.154|0.155|0.478|0.277|0.167|\n|Temperature|0.875|0.901|1.497|2.354|0.908|0.912|\n|Rain|0.096|0.051|0.146|0.610|0.598|0.477|\n|NN5|0.687|0.936|1.485|1.117|0.722|0.745|\n|Fred-MD|0.075|0.138|0.369|0.180|0.163|0.151|\n|Exchange|0.165|0.327|0.831|1.092|0.341|0.135|\n\n\n### DS\n\n|Dataset|BRIDGE|TimeVQVAE|TimeGAN|GT-GAN|TimeVAE|Diffusion-TS|\n|-|-|-|-|-|-|-|\n|Electricity|0.306|0.367|0.351|0.434|0.376|0.370|\n|Solar|0.156|0.231|0.258|0.202|0.199|0.236|\n|Wind1|0.188|0.247|0.323|0.295|0.218|0.203|\n|Traffic|0.037|0.112|0.195|0.192|0.225|0.198|\n|Taxi|0.402|0.363|0.409|0.5|0.411|0.391|\n|Pedestrian|0.246|0.252|0.250|0.287|0.370|0.299|\n|Air|0.093|0.180|0.197|0.238|0.202|0.267|\n|Temperature|0.216|0.262|0.292|0.298|0.293|0.275|\n|Rain|0.199|0.242|0.203|0.396|0.246|0.275|\n|NN5|0.274|0.5|0.5|0.491|0.454|0.445|\n|Fred-MD|0.326|0.343|0.5|0.336|0.389|0.355|\n|Exchange|0.228|0.290|0.343|0.332|0.276|0.287|\n\n\n **J-FTSD**\n\n|Model|Electricity|Wind|Traffic|Taxi|Pedestrian|Air|Temperature|Rain|NN5|Fred-MD|Solar|Exchange|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|BRIDGE|0.538|5.011|0.570|0.974|0.488|0.6536|3.977|0.132|0.972|0.260|0.295|1.581|\n|BRIDGEw/otext|1.821|6.935|0.611|1.312|0.550|0.677|4.708|0.151|1.192|0.423|0.330|1.738|\n|BRIDGEw/oprototype|1.164|6.843|0.597|1.0367|0.662|0.817|4.613|0.141|1.115|0.341|0.322|1.846|\n\n\n**[Q2] Provide more information about semantic prototypes.**\n\nFundamental time series properties for TS generation align with decompositions of TS, such as trends and seasonalities. We use semantic prototypes as latent representations of these commonalities and train the model to decode from combinations of these latent representations for generating certain domain. \n\nThe prototype array are fixed after initialization, similar to a initializing a codebook for vector quantization while preventing the codebook from updating along with the decoder model. Instead of updating prototypes, the decoder $\\epsilon_\\theta$ learns to map them to observed sequences, ensuring implicit alignment with time series semantics.\n\n**[Q3] Provide comparisons with Diffusion-TS**\n\nPlease see **[Q 1]** for Diffusion-TS results. \n\n**[Q4] What motivated the detailed intra-group discussions in the multi-agent setup?**\n\nOur design draws from the well-established **multi-agent cooperation and competition paradigms**, which enhance system performance by encouraging agents to challenge and refine each other’s outputs, mitigating the risk of overconfidence.\n\nAdditionally, complex problem-solving is often modeled as programming tasks due to their logical structure. Inspired by this, we structured our system after software development workflows, assigning roles like **manager, engineer, scientist, and observer** to different agents [1].\n\n[1] LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead \n\n**[Q5] Use Time-MMD dataset in TS generation**\n\nWe achieves **the best performance on MDD, KL, PS and DS across all subdatasets.** Due to space limitations, we only present the MDD results below.\n\n|Dataset|BRIDGE|TimeVQVAE|TimeGAN|GT-GAN|TimeVAE|Diffusion-TS|\n|-|-|-|-|-|-|-|\n|Agriculture|0.416|0.729|1.031|0.744|0.969|0.950|\n|Climate|0.185|0.391|0.387|0.530|0.209|0.260|\n|Economy|0.855|1.292|1.823|1.509|1.055|1.107|\n|Energy|0.272|0.414|0.561|0.468|0.328|0.390|\n|Environment|0.191|0.194|0.431|0.281|0.460|0.432|\n|Health(US)|0.248|0.343|0.649|0.430|0.298|0.355|\n|Security|0.344|0.355|0.850|0.941|0.824|0.923|\n|SocialGood|0.292|0.343|0.688|0.503|0.436|0.521|\n|Traffic|0.641|0.961|1.059|1.153|0.937|0.93|\n\n**[Q6] Does z correspond to the semantic prototypes?**\n\nAs stated in Section 3, z is a latent variable sampled from a prior distribution. In the diffusion model, z typically represents random Gaussian noise.\n\n---\n\nAgain, we sincerely appreciate the reviewer’s valuable time and insights, which have greatly strengthened our work. Based on your suggestions, we have added **PS, DS, and J-FTSD** metrics, included **Diffusion-TS** as a baseline, and expanded experiments on **Time-MDD**. We also appreciate the recommended references and will discuss them in our revision.  \n\nWe’d be happy to address any further questions—thank you for your thoughtful feedback and support!", "claims_and_evidence": "The claims regarding the effectiveness of concise text conditions, semantic prototypes, and their effects on sample fidelity are justified with suitable empirical evidence.", "methods_and_evaluation_criteria": "Yes, the proposed method is evaluated on a diverse set of 10 in-domain and 2 out of domain datasets for generation.\n\nHowever, the commonly used metrics for identifying sample quality, like the Predictive Score and the Discriminative Score [1], are not used in the paper.\n\nAdditionally, I am not sure about the pairwise mse score, as the generative process is stochastic, and additionally, for a single textual condition, there could be more than one suitable sample as the generative model essentially samples from the conditional distribution. Perhaps metrics like J-FTSD [2] are more suitable for evaluation in this case.\n\nFinally, there are multimodal datasets like TimeMMD [3] which can also be used to evaluate the generative model's performance.\n\n[1] Time-series Generative Adversarial Networks (TimeGAN)\n[2] Time Weaver: A Conditional Time Series Generation Model\n[3] Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis", "theoretical_claims": "There are no theoretical claims.", "experimental_designs_or_analyses": "Yes, the generation results in Tables 2 and 4 for in-domain datasets and the results in Tables 3 and 5 for out of domain datasets are valid. \n\nHowever, the comparison against recent diffusion-based time series generation approaches, such as Diffusion-TS, is missing.", "relation_to_broader_scientific_literature": "The automated dataset curation approach holds a lot of promise for enhancing the cross-modal alignment between text and time series for various tasks such as forecasting, generation, anomaly detection, etc.", "essential_references_not_discussed": "The related works with respect to diffusion models is not entirely covered in the paper. \n\n[1] Non-autoregressive Conditional Diffusion Models for Time Series Prediction\n[2] MULTI-RESOLUTION DIFFUSION MODELS FOR TIME SERIES FORECASTING\n[3] Time Weaver: A Conditional Time Series Generation Model\n[4] CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation\n[5] Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models\n\nSimilarly, the related work with respect to multi-modal datasets that include text paired with time series is not discussed. For example, Time MMD [6] is a recent multi modal dataset.\n\n[6] Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis", "other_strengths_and_weaknesses": "Strengths:\n1. The proposed approach shows strong in-domain performance and out-of-domain generalization.\n2. The paper provides detailed ablation studies to delineate the performance improvements due to the textual input and the semantic prototypes. \n\nWeaknesses:\n1. I am not convinced with the choice of metrics used for evaluating generative models. Please check the Methods and Evaluation Criteria for more discussion.\n2. The semantic prototypes provide significant performance boost. However, very little information is provided regarding how the prototypes are generated. The appendix suggests these are random orthogonal vectos. Can the authors provide more clarity on this?\n3. The ablation studies provide sufficient evidence for the necessity of multi-agent setup for data collection. However, what is the reason behind such detailed intra-group discussion with a scientist, engineer, etc? Can the authors provide more information regarding this?\n4. The paper could also use the Time-MMD dataset to show the effectiveness of using text in time series generation.", "other_comments_or_suggestions": "In the problem formulation section, the variable $z$ seems to appear suddenly, and it is not clearly explained. Does $z$ correspond to the semantic prototypes?", "questions_for_authors": "Please refer to the weaknesses section.\n\nOverall, It'd be very helpful if the authors can \n\n1. Justify the use of metrics\n2. Provide more information about semantic prototypes\n3. Provide some empirical results with comparisons against recent diffusion-based generation approaches like Diffusion-TS.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qlKNQaLgu7", "reviewer": "Reviewer_cEkx", "summary": "This paper introduces BRIDGE, a framework for text-controlled time-series generation (TSG) using a multi-agent system and diffusion modeling. The authors propose a three-stage process to generate high-quality text-time series pairs, and develop a hybrid framework that combines semantic prototypes with text descriptions. Experiments across 12 datasets show state-of-the-art generation fidelity and improved controllability compared to no-text approaches.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[Q1] What motivated the choice of 16 prototypes, and how does performance change with different prototype counts?**\n\nFor selecting the optimal number of prototypes, we observe from the ablation study that the generation performance generally improves along with the increase of the number of prototypes (Shown in Table). However, when the number of prototypes is larger than 16, the performance gain is no longer significant. Therefore, we can conclude that the optimal number of prototypes is 16 in our current setting.\n\nMDD Report:\n\n|Dataset|4|8|16|32|64|\n|-|-|-|-|-|-|\n|Electricity|0.615|0.368|0.135|0.117|0.236|\n|Wind|0.271|0.299|0.304|0.314|0.309|\n|Traffic|1.211|0.287|0.315|0.349|0.411|\n|Taxi|1.008|0.433|0.338|0.371|0.384|\n|Pedestrian|1.599|0.921|0.576|0.554|0.552|\n|Air|0.611|0.393|0.418|0.515|0.544|\n|Temperature|0.487|0.317|0.310|0.330|0.307|\n|Rain|5.763|4.981|6.002|5.548|6.420|\n|NN5|1.550|0.796|0.613|0.616|0.573|\n|Fred-MD|0.407|0.241|0.228|0.245|0.346|\n|Solar|375.536|375.531|375.531|375.532|375.556|\n|Exchange|0.365|0.359|0.315|0.309|0.330|\n\nK-L report:\n\n|Dataset|4|8|16|32|64|\n|-|-|-|-|-|-|\n|Electricity|0.006|0.027|0.001|0.001|0.020|\n|Wind|0.059|0.074|0.056|0.075|0.093|\n|Traffic|0.200|0.018|0.021|0.022|0.027|\n|Taxi|0.154|0.013|0.003|0.020|0.019|\n|Pedestrian|0.075|0.022|0.009|0.003|0.002|\n|Air|0.010|0.009|0.005|0.006|0.009|\n|Temperature|0.113|0.025|0.020|0.017|0.042|\n|Rain|0.014|0.009|0.010|0.008|0.018|\n|NN5|0.130|0.118|0.004|0.005|0.009|\n|Fred-MD|0.012|0.015|0.006|0.009|0.030|\n|Solar|0.025|0.016|0.005|0.008|0.017|\n|Exchange|0.062|0.063|0.067|0.083|0.046|\n\n\n**[Q2] How does your work differ from exist agent framework ? Could those be adapted for your task?**\n\nMetaGPT follows structured workflows tailored for software engineering tasks, with predefined agent roles and Standard Operating Procedures (SOPs). While effective in that domain, this structure is less readily adaptable to the dynamic, iterative processes needed for generating diverse and semantically aligned text–time-series pairs. AutoGen offers more flexible agent coordination, but lacks built-in support for evaluating alignment between text and time-series data. CAMEL, which focuses on role-playing agents for social simulations via inception prompting, is primarily designed for open-ended tasks rather than modality-aware data construction.\n\nAlthough these frameworks offer valuable capabilities in their respective domains, adapting them to text-to-time-series generation (TSG) would require substantial modification. In contrast, BRIDGE is purpose-built for TSG, integrating task-specific evaluation modules and iterative refinement mechanisms to support controllability and fidelity.\n\nTo better understand their applicability to TSG, we conducted preliminary adaptations of MetaGPT, AutoGen, and CAMEL within our task setting. As shown in the results below, their performance under minimal adaptation was relatively limited.  These observations highlight the utility of BRIDGE’s tailored design in addressing the unique demands of TSG, such as modality bridging.\nDue to space limitations, we only present the MDD results below.\n\n|Model|Electricity|Wind|Traffic|Taxi|Pedestrian|Air|Temperature|Rain|NN5|Fred-MD|Solar|Exchange|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|MetaGPT|0.353|0.327|0.330|0.363|0.727|0.464|0.373|7.561|0.712|0.434|390.901|0.428|\n|AutoGen|0.287|0.329|0.358|0.443|0.598|0.494|0.335|7.386|0.661|0.362|377.334|0.425|\n|CAMEL|0.480|0.385|0.386|0.813|0.905|0.889|0.509|5.553|0.855|0.311|393.671|0.355|\n|BRIDGE|0.143|0.276|0.246|0.325|0.552|0.438|0.323|5.129|0.570|0.226|375.530|0.312|\n\n\n#### **General Comments (GC):**\n**[GC 1] More discussion of privacy risks when generating time series in sensitive domains, such as healthcare.**\n\nThank you for highlighting the potential privacy risks in sensitive domains such as healthcare. While synthetic data is often considered a privacy-preserving alternative to real patient records, there is still a potential risks remain if models overfit and replicate training patterns.\nOur approach mitigates this risk by focusing on controllable and abstract generation guided by semantic prototypes and textual descriptions, rather than directly replicating raw time-series trajectories. Additionally, our framework does not rely on any personally identifiable attributes—only descriptions of the time series are used during generation. We fully agree that a more in-depth analysis of privacy risks—such as membership inference—would be valuable, and we plan to explore these directions in future work.\n\n--- \n\nWe are grateful for your engagement and helpful suggestions, which we will carefully incorporate in the revised version.", "claims_and_evidence": "The claims are partially supported by evidence. While performance improvements are demonstrated across multiple datasets, the paper lacks:\n\nTheoretical justification for the 16 semantic prototypes design choice\nComprehensive comparison with other multi-agent frameworks (e.g., MetaGPT, CAMEL，Autogen)", "methods_and_evaluation_criteria": "The methods and evaluation criteria are appropriate. The authors:\n\nUse standard metrics (MSE, MAE, MDD, K-L divergence) for quantitative evaluation\nInclude human evaluation to assess the quality of generated time series", "theoretical_claims": "No major theoretical claims to verify. The paper is primarily empirical with focus on the practical implementation and evaluation of the proposed framework.", "experimental_designs_or_analyses": "The experimental designs are generally sound, but with limitations:\n\nThe performance comparison between text types could benefit from more rigorous statistical analysis\nThe effect of different numbers of prototypes is mentioned but not systematically explored", "relation_to_broader_scientific_literature": "The work connects to several research areas:\n\nExtends text-to-X generation from images/videos to time-series data\nBuilds upon diffusion models for continuous data generation\nIncorporates multi-agent collaboration concepts, though without deep engagement with that literature", "essential_references_not_discussed": "Missing references include:\n\nRecent multi-agent frameworks like MetaGPT, AutoGen  and CAMEL that offer more sophisticated agent interaction mechanisms.", "other_strengths_and_weaknesses": "Strengths:\n\nAddresses an important practical problem with real-world applications\nThe hybrid approach combining semantic prototypes with text shows promise for cross-domain generalization\n\nWeaknesses:\n\nLimited theoretical foundation for the semantic prototype design\nThe multi-agent framework appears to be a straightforward application of role-based prompting rather than a novel contribution", "other_comments_or_suggestions": "The paper would benefit from more discussion on potential privacy concerns when generating synthetic time series data from sensitive domains like healthcare.", "questions_for_authors": "Why were 16 semantic prototypes selected? Did you experiment with different numbers, and how does the performance vary with the number of prototypes?\n\n\nHow does your multi-agent framework compare with other established frameworks like MetaGPT or CAMEL? Could those frameworks be adapted to your task?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zSk7TIuXW3", "reviewer": "Reviewer_3kGj", "summary": "This work introduces (1) \"Text Controlled TSG\" a new TSG task, (2) a new LLM/MA framework to synthesize datasets, and (3) BRIDGE, a hybrid TSG framework building off the prior two contributions", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the thoughtful and encouraging feedback. We are especially grateful for your recognition of the clarity of the paper, the natural design of the proposed methods, and the strength of our empirical results and discussion. Your comments affirm the value of our effort to introduce a novel task—Text-Controlled Time-Series Generation (TSG)—and the corresponding framework BRIDGE, which combines a multi-agent dataset synthesis pipeline with a hybrid diffusion-based generation model.\n\nYour summary precisely captures the key contributions of our work: (1) the introduction of a new task—Text-Controlled Time-Series Generation (TC-TSG)—to enable semantically guided, controllable synthetic time series; (2) the design of a novel multi-agent framework, which leverages large language models (LLMs) and role-based collaboration for bootstrapping high-quality text-to-time-series paired data; and (3) the proposal of BRIDGE, a hybrid framework that integrates semantic prototypes and text for flexible and faithful time-series generation.\n\nIn particular, we are grateful for your acknowledgement that our empirical findings convincingly support both the fidelity and controllability improvements claimed. Your validation that the evaluation criteria and dataset choices are natural and grounded in prior literature is also very helpful.  This motivates us to continue refining and extending this line of work. In future iterations, we plan to further investigate theoretical insights behind controllable generation, and explore more efficient multi-agent optimization strategies, building on the foundation established here.\n\nShould you have any suggestions or requests for clarification, we would be more than happy to provide additional details. Thank you again for your constructive and generous evaluation—it is deeply appreciated.", "claims_and_evidence": "The authors claim (1) this approach achieves state of the art fidelity on 11 of the 12 datasets and (2) improves controllability compared to no text input generation. Both are borne out from the empirical results.", "methods_and_evaluation_criteria": "The methods are natural given the problem the authors are attempting to solve (high-fidelity and controllability in TSG). The evaluation datasets appear to be natural choices as well, and standard w.r.t. prior literature.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "N/A", "relation_to_broader_scientific_literature": "This work is related to prior work using text for TS analysis, and also other conditional TSG works. Relevant works in each appear to be properly cited and contextualized.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strengths\n- The paper is clear, well-structured, and easy to read\n- The techniques employed all appear to be natural choices\n- The empirical results are convincing\n- The discussion is thorough and insightful\n\nWeaknesses\n- Nothing major", "other_comments_or_suggestions": "N/A", "questions_for_authors": "N/A", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper introduces a framework for text-controlled time-series generation, tackling the important challenges of controllability and data scarcity in this domain. One of the main contributions in the paper is the LLM-based multi-agent system designed to bootstrap high-quality text-to-time-series paired datasets. The authors present interesting empirical results across various datasets, demonstrating state-of-the-art generation on most datasets and significant improvements in controllability compared to unconditional generation or generation without text. Reviewers generally said the task formulation was novel and that the combination of multi-agent systems and diffusion models is interesting. Initial reviews raised some concerns about the evaluation metrics (e.g., a lack of standard generative metrics), as we as comparison to recent related work (e.g., Diffusion-TS, Time-MMD). 
The authors provided a thorough rebuttal, addressing most of their concerns. Particularly, they added extensive experiments, new metrics (PS, DS, J-FTSD), comparisons to requested baselines/frameworks, and detailed clarifications that successfully addressed most major concerns raised by the reviewers.  While minor points regarding the specific implementation choices still remain, the overall methodology is sound, technically solid, and well-executed. Overall, this paper represents a solid contribution to controllable time-series generation.
Decision: Accept (poster)