SciTS: Scientific Time Series Understanding and Generation with LLMs
Authors: Wen Wu
Ziyang Zhang
Liwei Liu
Xuenan Xu
Jimin Zhuang
Ke Fan
Qitan Lv
Junlin Liu
Chen Zhang
Zheqi Yuan
Siyuan Hou
Tianyi Lin
Kai Chen
Bowen Zhou
Chao Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: time series
large language model
benchmark
Primary area: datasets and benchmarks
TL;DR: We introduce SciTS, a comprehensive scientific time-series benchmark, and TimeOmni, an LLM-based framework for time series understanding and generation.

Abstract: The scientific reasoning ability of large language models (LLMs) has recently attracted significant attention. Time series, as a fundamental modality in scientific data, presents unique challenges that are often overlooked in current multimodal LLMs, which either encode numerical sequences as text or convert them into images. Such approaches may be insufficient for comprehensive scientific time series understanding and generation. Existing unified time series models typically specialise in either forecasting or analysis, and their effectiveness on non-periodic, heterogeneous scientific signals remains unclear. To address these gaps, we introduce SciTS, a benchmark spanning 12 scientific domains and 43 tasks, with over 50k+ instances, both univariate and multivariate signals ranging from $10^0$ to $10^7$ in length and up to 10~MHz in frequency. We benchmark 17 models, including text-only LLMs, multimodal LLMs, and unified time series models, and find that general-purpose LLMs exhibit stronger generalisability than specialised time series models, while representing time series as text or images limits their performance due to excessively long sequences and loss of numerical precision, respectively. We then introduce TimeOmni, a working example to explore insights into how LLMs can be extended to handle scientific time series while remaining compatible with general-purpose LLM training. This work fills a gap in both dedicated benchmarks and illustrative frameworks for scientific time series, paving the way for LLMs to understand and generate complex temporal scientific data.

Published as a conference paper at ICLR 2026
SCITS: SCIENTIFIC TIME SERIES UNDERSTANDING AND GENERATION WITH LLMs
Wen Wu', Ziyang Zhang Liwei Liu Xuenan Xu' Jimin Zhuang 1.2 Ke Fan
Qitan Lv 1.4 Junlin Liu' Chen Zhang 1.2 Zheqq Yuan 1.2 Siyuan Hou Tianyi Lin Kai Chen', Bowen Zhoul Chao Zhang
'Shanghai Artificial Intelligence Laboratory, "Tsinghua University Harbin Institute of Technology, *University of Science and Technology of China (wuwen, zhangchao} @ jlab. org.cm cn
ABSTRACT
The scientific reasoning ability of large language models (LLMs) has recently at- tracted significant attention. Time series, as fundamental modality in scientific data, presents unique challenges that often overlooked in current multimodal LLMs, which either encode numerical sequences as text or convert them into im- ages. Such approaches may be insufficient for comprehensive scientific time series understanding and generation. Existing unified time series models typically spe- cialise in either forecasting or analysis, and their effectiveness on non-periodic, heterogeneous scientific signals remains unclear. To address these gaps, we in- troduce SciTS, benchmark spanning 12 scientific domains and 43 tasks, with over 50k+ instances, both univariate and multivariate signals ranging from 10 to 10" in length and up to 10 MHz frequency. We benchmark 17 models, includ- ing text-only LLMs,, multimodal LLMs, and unified time series models, and find that general-purpose LLMs exhibit stronger generalisability than specialised time series models, while representing time series as text images limits their per- formance due to excessively long sequences and loss numerical precision, re- spectively. We then introduce TimeOmni, working example explore insights into how LLMs can be extended to handle scientific time series while remaining compatible with general-purpose LLM training. This work fills gap in both ded- icated benchmarks and illustrative frameworks for scientific time series, paving the way for I.LMs understand and generate complex temporal scientific data
INTRODUCTION
Large language models (LLMss) have demonstrated remarkable capabilities natural language un- derstanding. reasoning, and generation, and their potential for scientific applications has recently attracted increasing attention (Yang et al.] 2025a Zhou et al. 2025; Bai et al.] (2025)). Scientific data, however, is often multimodal, and time series represents widely encountered modalities across disciplines such as physics, astronomy, biology, and engi- neering. Understanding and generating scientific time series critical for analysing diverse signals, such as astronomical light curves, neural recordings, and bioacoustic vocalisations, as well as tasks such stellar collision detection and forecasting physiological states. However, scientific time ries remain largely under-explored for LLMs. Bridging this gap is pivotal to transforming LLMs from narrative assistants into powerful engines that can interrogate dynamical systems, drive scien- tific discovery, and guide experimental design.
of the most fundamental and
Current multimodal LLMs typically handle time series by either directly encoding numerical se- quences as text or converting signals into images (Wang et al. 2025c Bai et al.) 2025b; Zhou et al.) 2025). Representing time series as text leads to excessively long sequences that LLMs struggle
Cooresponniig author.
' benchmark is available at: http::/:/hhggigggac..c//eaaaaaa htt.a/aaa..//aaaaaaa////aaaaaaaa TimeOmni framework is available https://github, com www./pemn//..aaaaeeeeeeepmmm memmmni

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Published as a conference paper at ICLR 2026
to process, while image-based based representations sacrifice numerical precision. As a result, these ap- proaches may fail to capture the rich temporal dynamics, long-range dependencies, and domain- specific patterns inherent in scientific time series, limiting the models' ability to perform both com- prehensive analysis and accurate generation. On the other hand, models specifically designed for time series have traditionally been developed for individual tasks, domains, datasets, limiting their applicability beyond narrow settings. Unitying time series modelling across diverse domains non-trivial, since signals exhibit varying scales, frequencies, dimensions, lengths, and dynamic patterns. Existing unified time series models make progress toward cross- domain applicability but typically focus on specific task types, such as forecasting (Shi et al. 2025 Din al., 2024) Wang et al. 2025b; Woo et al.) 2024) or analytical tasks (Xie et 2005), ) and their effectiveness on non- periodic, heterogeneous scientific signals remains unclear. Moreover, these models often rely on specialised architecture designs that are not readily incorporated into LLMs to enhance their ability to handle time series.
To address these gaps, we first introduce SciTS, large-scale benchmark for scientific time ries understanding and generation. SciTS comprises 54,023 instances spanning 43 domain-specific tasks across 12 scientific disciplines: Astronomy, Bioacoustics, Earth Science, Economics, Energy, Manufacturing, Mathematics, Meteorology, Neuroscience, Physiology, Radar, and Urbanism. The benchmark covers both univariate and multivariate signals, with lengths ranging from 10" 107 and frequencies up to 10 MHz, and includes 7 major task types: anomaly detection, classification, multiple-choice question answering. event localisation, forecasting, imputation, and synthesis.
We benchmark 17 state-of-the-art models on SciTS, spanning text-only and multimodal LLMs (open- and closed-weight) as well as unified time-series models. SciTS is highly challenging: in- stances exceed current system limits, leading to context-length overruns and followi failures. While LLMs generalise better to scientific domains than specialised time series models, se- rialising temporal signals as text or rasterising them as images constrains performance. TimeOmni attains the top rank, underscoring the advantage of explicitly modelling temporal dynamics within an L.M and the promise of LL.M-based solutions for complex scientific time series.
In addition, we introduce TimeOmni as working example to explore the key ingredients that may be necessary for unified scientific time series understanding and generation. TimeOmni leverages the reasoning and knowledge LLMs while explicitly modelling temporal dynamics. Given an input time-series signal and a task prompt. it generates textual responses time-series outputs. In order to handle diverse signals with varying lengths, resolutions, and dimensions, TimeOmni employs multiple patch experts with novel routing mechanism that automatically selects the most suitable patch expert. The framework is intended to be readily incorporated into general-purpose LLMs for joint training with other modalities and tasks.
We introduce SciTS, to our knowledge the most comprehensive benchmark to date for scientific time series, spanning a wide range of domains, tasks, and signal types. 2) We conduct a large-scale evaluation on SciTS, revealing key challenges in generalising to scientific domains and efficiently handling scientific time series. 3) We introduce TimeOmni to study what is required for LLMs to handle diverse time-series data, revealing insights into explicit and adaptive temporal modelling.
Summary of Contributions. 1)
2 RELATED WORK
Science Time Series Benchmarks. Time series benchmarks have been developed for tasks such as forecasting, anomaly detection, and question answering (QA) across domains including finance,
weather, and traffic (Cai et al. 2024 Chen et al.) 2025b) Liu et al. 2024b Kong et al.) 2025
However, scientific domains such as astronomy and earth science have not been extensively stud- ied. Current scientific benchmarks for LLMs, such Scientists' First Exam (Zhou et al. 2025),
ScienceQA (Lu et al., 2022). and CHARTQAPRO (Masry et al., 2025). focus on reasoning over text and images, resulting in information loss when temporal data is converted into visual formats. SeiTS addresses this gap by providing a large-scale, multi-domain benchmark focused on scientific time series understanding and generation, comparison to existing benchmarks shown in Table
Representation Learning for Time Series. Developing unified time series modelling remains an open challenge. Time series span diverse domains and exhibit varied scales, sampling rates, and
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Table 1: Comparison of SciTS and existing time series related benchmarks.
Dataset
Input Format
Tasks
Scientific
Domains
Data Source
Multivariate
Size
SFB
Image
2
Real
/
830
Time
TimeSeries
5
Synthetic Real Real Real Real & SSnthhttic
X
700
Time-MQA
TimeSeries
3
200,000 X
Time-MMD
TimeSeries
X
X
17,113
MTBench
TimeSeries
3
X
X
20,000
SciTS
TimeSeries
7
V
12
54,023
Time Series Data
Science Disciplines
Wide Range of Frequencies & Longcha
Multi- dimersioonl
stossladery
cldl
the
Datier
Varieus Data Formats
and
Eexoories
SciT's
caty App. Ala,
SciTs
Mariooset
Code
Extonnin
Eaerpo
Description
Blrc
pma.
Classift cation
Anemaly
Trecio
Wealher
MCR
Detection
Locolisation
Imputation
Forecasting
Syntheais
Tasks
Figure 1: Twelve scientific disciplines included SciTS.
Figure 2: Overview of SciTS, covering diverse frequencies, lengths, dimensions, and 7 task types.
dynamic patterns, complicating representation learning and posing challenges for models to adapt across tasks and domains. Transformer- beasffrrmer-bbeed architectures form the backbone of unified time series models, including encoder-focused designs (Woo et al.) 2024), decoder-based structures (L.uu al.) 2025 Ansari et al. 2024; Wang et al. (025b), and their extensions with mixture-of-experts (Xu al. 2025a Shi al., 2025). LLMS have been recently explored for time series analysis, though their effectiveness remains debated: LLM-based forecasters often perform no better than strong non-LLM baselines, highlighting the difficulty of transferring linguistic pretraining to temporal pre- diction (Tan et al. 2024 Zhou & Yu (025). To bridge this gap, multimodal approaches treat time series as an additional modality (Xie et al., 2025). and alignment-baad based methods adapt temporal signals into compaielcoomatible forms (Hu et al. 2025 Zhang al. 2025 Jin al.. 2024). De- spite these advances, existing approaches largely focus on either understanding or generation, rather than a unified treatment of both. UniTS (Gao al. 2024) makes step forward by integrating QA and forecasting tasks, while it relies on separate architectural design, not compatible with general- LLM training. Moreover, scientific signals, which are often heterogeneous and non-periodic, remain unexplored. In this work, we propose TimeOmni, a framework that unifies types of understanding and generation tasks, and can be readily integrated into LLMs for joint training with other modalities and tasks. Extended discussion on the related work is provided in Appendix B
3 SCITS DATASET AND TASKS
The SciTS benchmark consists of 54,023 samples spanning 43 tasks across 12 disciplines: Astron- omy, Bioacoustics, Earth Science, Economics, Energy, Manufacturing, Mathematics, Meteorology, Neuroscience, Physiology, Radar, and Urbanism, shown in Fig. Time series from different domains exhibit widely varying characteristics: frequencies range from once per day in light curve observations to kilohertz for bird chirps in bioacoustics, and even megahertz in radar communica- tions; lengths vary from fewer than 10 points for sait-freezing measurements to millions of points for animal sounds; moreover, SciTS includes multivariate signals, with the number of channels reaching up to 58 for electroencephalogram (EEG) recordings in neuroscience. Detailed dataset statistics are presented in Table 2 and more details can be found in Appendix D.2
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3.1 DATA COLLECTION
SeiTS data was collected from a combination of open-source datasets, scientific domain websites, and numerical simulation methods widely used for relevant domains. Tasks were designed by lever- aging the meta-information accompanying the data, which guided how inputs and target answers were defined. These tasks can be broadly grouped into two categories: understanding, where mod- els answer questions given time series signals, and generation, where models produce time series conditioned on questions or question- signal pairs. The target answers all cases were derived from the original meta-information or time series themselves, ensuring consistency with the underlying scientific data. All tasks were unified under a prompt-based format to ensure compatibility with LLM-style training and evaluation. Finally, systematic quality checks were conducted to verify the consistency, correctness, and scientific validity of the data.
3.2 DOMAINS
SciTS covers wide range of scientific domains, each with distinct time series characteristics and tasks. Astronomy, tasks involve detecting col- lapse or collision events and predicting merger times based on gravitational wave data, and classifying ce- lestial objects based on light curve variability. In Earth science, tasks involve detecting carthquake events from seismic wave data and, if an event curs, predicting the arrival times of P-waves and S-waves. In Bioacoustics, tasks involve classify- ing the species of birds or other animals based on their vocalizations, and identifying the type of mar- moset call from recorded sounds. In Meteorology, tasks involve abnormal environmental event detec- tion from parameters such as temperature, humid- ity, wind speed, and soil moisture, and precipitation forecasting based historical measurements. Eco- nomics data consist of equity price series combined with news for trend prediction and causal analysis. In Neuroscience, tasks involve detecting neurologi- cal psychiatric conditions from electroencephalo- gram (REG), classifying ERG patterns, predicting or imputing future EEG signals, identifying motor imagery, and sleep staging. In Energy, tasks in- volve short- and long-term prediction of power system behaviour based on half-hourly energy con- sumption readings, minute-by-minus wind power production, and hourly transformer sensor mea- surements. In Physiology. tasks involve analysing electrocardiogram (ECG) recordings to assess health status or detect abnormalities, recognising gait freezing from wearable accelerometer data, performing general activity recognition from multi-axis accelerometer measurements, and predict- ing or imputing physiological signals such as slow cortical potentials, heart rate, arterial or venous pressures, and atrial fibrillation events. In Urbanism, tasks involve predicting pedestrian activity from multi-sensor counts, forecasting traffic flow using historical measurements and relevant news, and detecting anomalies in traffic system data, including sudden surges sensor malfunctions. In Manufacturing, tasks involve analysing vibration signals from industrial bearings to determine the size and location of damaged components, well as detecting anomalies malfunctions from oper- ational recordings. In Radar, tasks involve classifying radar activities based on coding schemes and categorising operational modes and communication modulations. In Mathematics, tasks involve forecasting the evolution of multivariate chaotic systems based on given input.
Table 2: Statistics of SciTS
Statisties
Number
Discipline
12
Domain tasks # Instances
43
54,023
Understanding tasks
32,149
# Classification
18,364
MCQ Anomaly detection
716
13,069
Generation tasks
21,874
# Forecasting Imputation
15,593
2,014
Event localisation
1.567
Synthesis
2,700
Numerical Value Range Frequency Range (Hz)
10 ~ 10
10-5 107
Dimension Range
~ 58
Length Range
10" 107
3.3
TYPE OF TASKS
SeiTS covers major types of tasks, described as follows. Understanding tasks include anomaly detection, where models determine whether abnormal events occur in given time series; classifica- tion, where models determine which category (or categories) the input series belongs to from a given set; and multiple-choice question answering (MCQ), where models given a time series and
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Output Time Series
Text Token
Processing Multi-dimensionll Signal:
Regression Heade>
Channel 2
Softmax
Flatten
(Flatten & Lincar)
Channel
3
2
3
4
6
Time Series Embeddings
Pretrained LLM
Patch Reprogramming
Patch Expert
Putch Expert
Patch Expert N
(Patch size
(Putch
or
size
Time Series Encoder
Embodding Layer
Text Tokenizer
Router
7/100)
Task Prompi
Length: T
Input Time Series
Figure 3: The overall architecture of TimeOmni. An input time series is processed by the Time Series Encoder, where a Router selects an appropriate Patch Expert, and embeddings are refined via Patch Reprogramming. the Embedding Layer of LLM. The time series and prompt embeddings are concatenated and fed into the Pretrained LLM. Two output heads used: (i) understanding tasks produce text via softmax layer, and (ii) generation tasks generate time series through flattening and linear transformation. The way to process multi-dimensional signals are shown in top left.
parallel, the Task Prompt is processed through the Text tokeniser and
question with four options (A-D) and must select the correct answer. Generation tasks involve event localisation, identifying the timestamps of specific events within time series; forecasting, predict- ing future values based on observed data; imputation, completing series with missing values; and synthesis, generating time series from textual descriptions. The inputs be context-dependent, including auxiliary text such as background information or news to guide the output, or context- independent, where only the time series is provided. Generated sequences range from short-term predictions of a few steps to long-tern tere-term forecasts of up to 720 steps. Further details on signal types, frequencies, and lengths for each task are provided in Appendix
4 TIMEOMNI: A WORKING EXAMPLE
To explore the key ingredients that may be necessary for extending current LLMs to handle scientific time series, we develop TimeOmni as an illustrative framework. The structure TimeOmni shown in Fig. 3 consisting of time series encoder, a LLM backbone, and output heads for diverse signals. Given an input time series signal X E 'TN' it is first flattened along the time dimension to obtain X' E NT'x1 The encoder automatically selects patch expert based on NT', producing Xemc E R.. The x Dti,, where Dim is the L.M hidden dimension and Tenc is typically 100 to 200. The task prompt tokenised and embedded into P E Rxxx..,, which is concatenated with X input to the LLM backbone. Output embeddings from the L.M are processed according to task type: a softmax layer to generate discrete tokens for understanding tasks and a regression head output a time series for generation tasks.
4.1 TIME SERIES ENCODER
As shown in Fig. time series is explicitly modelled by encoder, which consists of three main components: router, patch expert family, and a patch reprogramming module (Jin et al.) 2024). For flattened input of length T NT", the Router selects patch size D Dpatch to sure that the output sequence length after the Patch Expert layer falls between 100 and 200, i.e. T/200 Dpunch T/100. The Patch Expert first patchifies the signal, reshaping X' from Txx
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to R (TT//auss Paaach A I-dimensional (-dim) convolution then maps it to Xpach E E RT/Da Dov, where Dene is consistent across the Patch Expert family. The Patch Reprogramming module re- represents the time series using the LLM's vocabulary embeddings E &R vieah Pan, E is first projected through a linear layer to 121000x Dun, then X puch attends to E via multi-head cross- attention, with X putch query and E as key and value. A final linear projection produces the encoder output Xenc-
4.2 LLM INPUT AND OUTPUT
For understanding tasks, we use a Prompt-as-suffi strategy, where Xene is followed by P. For generation tasks, Prompt-as-prefix approach is adopted, placing P before X The concatenated vector is fed into the LLM. For understanding tasks, output embeddings of LLMs pass through a softmax layer to produce discrete text tokens, following the standard LLM generation process. For generation tasks, the output embeddings are flattened and mapped via a linear layer to the required length of the output time series. To handle the wide range of output lengths in the benchmark, we predefine a set of regression heads and the model selects the one with the closest length, truncation applied if necessary.
EXPERIMENTAL SETUP
General Settings. We conduct a comprehensive evaluation of various types of models on SciTS.
This includes (i) Text LLMs with closed and open weights: GPT-4.1-mint Gemini-2.5-Flaahh DeepSeck-V3 (Liu et al. 2024a), Llama3-8B (Grattafiori et al. 2024), Qwen3-4B (Yang et al. 2025a), Qwen3-8B (Yang et al.] 2025a); (ii) Multimodal LLMs with closed and open weights:
GPT GPin--mmni, 2.5- Intern LB35544B (Wang et al.] (025c), InternVL3.5-8B (Wang
al. 2025c), 2. (Bai et al. (025b); (iii) Unified time series models: Moirai- (Shi et al. 2025). Chronos-bolt-Base (Ansari et al.) ChaTS (Xie et al. 2025). UniTS (Gao al.) 2024). For text LLMs, time series are pro- cessed as sequences of digits in text form. For multimodal LLMs, time series are converted into an image. Compared to LLMs, unified time-series models are specifically tailored to time-series data, often trained across multiple domains and datasets, but typically specialise in particular applications such as forecasting. All models were assessed in zero-shot setting. Details can be found in Ap- pendix E TimeOmni is initialised with pretrained weights of Qwen3-8B (Yang et al., 2025a and finetuned using DoRA (Liu et al. 2024c). Implementation details can be found in Appendix
Large
Woo et al., 2024), TimeMoE-Lary
2024).
We present accuracy and FI for all understanding tasks. For generation tasks, we report mean absolute error (MAE), mean absolute percentage error (MAPE), success rate, and success-rate- weighted MAPE (swMAPE). MAPE measures the relative prediction error between predicted ÿ, ---&
Metrics.
and actual values y/s, expressed as percentage of the actual values: MAPE 100%
Li=l
Models can sometimes fail process a given input instance, particularly for generation tasks, e.g., when the input exceeds the model's maximum context window or when the model fails to follow instructions. To account for such cases, we report the success rate for each generation task, defined as the proportion of successfully processed samples relative to the total number of samples. We then define swMAPE as MAPE divided by the success rate, providing an overall performance measure for generation tasks that penalises failures. Note that all MAE and MAPE values are computed the original data scale, not on normalised (e.g., z-scored) inputs.
6 RESULTS AND DISCUSSION
We assess 17 models mentioned in Section SciT' across 43 domain-specific tasks spanning 12 disciplines, task types, and a wide range of frequencies, lengths, and multivariate signals.
6.1 TASK COVERAGE AND SUCCESS RATE
We first examine the ability of the assessed models to handle the diverse task set. As shown in Fig. 4aj closed-source LLMs, such as GPT-mini and Gemini2.5-Flash can perform most tasks using either text or image inputs. In contrast, open-source text-only L.MSs (e.g., Qwen, LLaMA) fail on

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LLM
Multimodal LLM
Time Series model
Timeommii(unr
7 Closed-source
Open
100-
Supported
40-
Success Rate (N)
20-
50
GeT-a, J-min
Goming Comin22.--lal Saai Deplekk- VI
Liamatinn on up
Owomd Qwemd 48
Comintz 5. Finl-y
GPP: miin
Inter InteaVLL 3. 3-48 SVL-7B
3.89
Moscial Larg
Time MoR. aoro
Chromee Bolk Bese
ChaTTS UWTS
Traet Traet oun))
GPT-4 I-iini Geninl 3.Pial Deplerk- 19
Liamst- -- all
Obent (beor 4B
Gomint InienV v.3...3.. 5.88
CPPS GPP:5-mi Saan
IntenVi 5-48
Qwraz SVL-7D
MuizusL Lage
Time o.d.d.oo arpe
Chrome Bolt Heso
CharTS
Times Timet
Uw779
Queaz
(a) Number tasks supported by cach model.
(b) The average success rate for supported task.
Figure 4: Task coverage (a) and average success rate (b) for models SeiTS. (a): fraction of supported domain tasks for each model. (b): average instance-level success rate for supported tasks.
approximately 10% of tasks. This is primarily due three factors: input sequences exceeding the maximum context length, which affects signals such as bioacoustic vocalisations and gravitational waves; (ii) failures to process multivariate signals such as EEG and generate multivariate sequences such as chaotic system parameters; (iii) failures to follow output instructions to generate sequences of the required length. This limitation is partially mitigated when time series are visualised as images for multimodal LLMs that accept image inputs, shown by higher task coverage.
While closed-soure source L.LMs support most task types, they still face instance-level limitations: han- dling task does not guarantee successful processing of every individual case. To quantify this, report success rates in Fig. (4b] showing that all LLM-based methods exhibit relatively low success rates at the instance level, with closed-source models generally outperforming open-source ones.
On the other hand, unified time series models typically focus on a certain type of task, resulting in low overall task coverage (Fig. 4ap. For example, Moirai and TimeMoE only support forecast- ing and cannot handle imputation tasks or tasks requiring text input. Despite this limitation, they achieve high success rates on the tasks they do support, demonstrating the effectiveness of spe- cialised models. The illustrated TimeOmni framework, in comparison, successfully handles all tasks and instances, achieving both full coverage and full success across the benchmark.
6.2
UNDERSTANDING TASKS
Table compares text-only LLMs, multimodal L.MSs, and time series models on understanding tasks across different scientific disciplines, with results reported as the average F1 score across tasks in each discipline. Overall, models achieve limited performance on scientific domains such as as- tronomy, neuroscience, and physiology, possibly due to these scientific domains being largely absent from the training data of general purppss models. Performance is particularly low for bioacoustics and radar, with F1 scores below 10,, highlighting the challenging nature of the SciTS benchmark. In contrast, nearly all models achieve strong performance domain is closer to general knowledge and thus falls within the scope of general-purpose models. It worth noting that for the urbanism domain, the results in Table primarily from Task 4 an anomaly detection task with relatively few instances. Most models effectively perform random guessing, and the highest Fl score (bolded) largely reflects bias toward a single class. In fact, GPT- 4.1-mini and Doven-2.5-V1 achieve higher accuracy with detailed results provided in Appendix [F
economics (except LLaMA3), this
Overall, multimodal LLMs that take images as input outperform text-only LLMs, possibly because tasks requiring high-level understanding do not strongly depend on precise numerical values, and representing time series as text can produce extremely long inputs that are difficult for LLMs to pro- cess. Converting time series into images compresses these long sequences more effectively. We also found models specialised for time series do not necessarily surpass general-purpose L.MSs across many domains, which indicates that when exposed to data domains unseen during training. LLMs demonstrate stronger generalisation capabilities. Across most domains, the illustrated TimeOmni framework achieves the overall best performance, with Gemini2.5-Flash ranking second.
Thhe mapping of task IDs their corresponding tasks is provided in Table

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Table 3: Performance understanding tasks of SciTS. Values show the average Fl (%) across tasks within each discipline (accuracy used for MCQ tasks). a model fails any task in discipline, the fraction completed (completed/total) is reported. Average ranking (AvgRk) computed by ranking models for each task and averaging across all understanding tasks.
Astron, Rioacous. EarthSci. Econ. Meteoro, Manuf. Neuro. Physio. Radar Urban. AvgRk
Large Language Models (Text Input)
GPT-4. -iiii
41.4
6.7
67.0
90.4
45.3
31.7
13.5
26.8
17.6
64.4
6.1
Gemini2.5-Flash
40.2
10.3
67.6
51.8
12.7
31.8
17.2
64.6
5.5
DeepSeek-V3
6.7
2.2
40.2
91.1
49,9
20.8
9.6
27.0
11.8
64.7
7.7
Llama3-8B
(1/2)
(0/3)
(0/1)
51.2
49.1
(0/3)
(0/4)
32.6
9.2
67.4
12.5
Qwen3-4B
(1/2)
(0/3)
46.5
77.9
40.9
(0/3
(1/4)
30.0
8.3
62.8
12.4
Qwen3-8B
(1/2)
(0/3)
47.2
83.5
54.3
(0/3)
(1/4)
33.4
6,5
66.2
11.8
Multimodal Large Longuage Models (Text
Image Inpid)
GPT-S-mini
42.3
10.7
67.6
83.8
45.3
38.4
13.9
25.0
16.5
64.8
6.0
Gemini2.5-Flar
38.4
7.7
72.5
82.9
52.3
39.4
29.8
27.3
21.4
67.0
5.8
InternV1.3.5-44
39.7
3.8
73.6
86.1
36.4
11.7
15.4
28.2
4.8
67.4
8.6
Intern 5B
21.3
4.7
54.0
23.9
30.0
3.1
67.4
6.9
Qwen2.5-VL-7B
7.1
9.3
80.6
81.9
28.1
6.0
16.7
5.9
58.5
9.5
Time Series Models
ChaTS
11.3
(0/3)
64.8
79.2
51.2
(2/3)
22.7
30,9
13.9
65.4
9.2
UniTS
38.2
8.1
0.0
27.1
9.8
48.5
25.9
22.9
10.6
67.4
7.9
TimcOmai (Ours)
73.2
58.1
82.5
96.4
61.3
82.0
60.1
45,9
68.9
64.8
1.9
Table Performance on generation tasks of SciTS. Values show the average success rate weighted MAPE (swMAPE) across tasks within each discipline. If model fails any task in discipline, the fraction completed (completed/total) is reported. Average ranking (AvgRk) computed by ranking models for each task and averaging across all generation tasks.
Astron. EarthSei.
Meteoro.
Econ.
Neuro.
Energy
Physio.
Urban
Math
AvgRk
Large Language Models (Text Imput)
GPT-4.1-mint
100.9
65.0
85.0
112.2
61.4
2.0c3
610.6
670.0
1.2c3
6.7
Gemini2.5-Fiam
116.6
63.0
107.5
4.5
38.7
307.6
60.5
391.4
477.5
4.6
DeepSeek-Vi V3
584.4
90.1
150.3
8.4
77.0
101.0
114.4
1.0e3
594.5
6.3
Llama3-8B
(0/1)
(0/1)
(0/1)
(0/2)
2.8e3
199.6
1.1e3
1.7e4
(0/1)
12.9
Qwen3-4B
(0/1)
7.9e4
(0/1)
(1/2)
153.9
110.0
293.7
1.4e3
(0/1)
9.9
Qwen3-8B
(0/1)
2.9c4
1.604
(1/2)
242.4
148.2
280.8
4.8c3
(0/1)
10.2
Mudtimodal Large Language Models (Text Image Input)
GPT-5-mini
55.4
25.4
72.6
(1/2)
1.1c3
(4/5)
230.9
558.0
1.5e3
8.1
Gemini2.5-Flash
108.7
24.6
142.5
10.8
330.5
(4/5)
4.9c3
2.1c3
521.0
9.8
VLt.5/L3..54pp
80.5
275.6
1.7e3
(0/2)
809.2
(4/5)
4.2e3
(3/4)
(0/1)
13.4
InternV1.3.5523
167.8
219.6
137.7
(1/2)
472.3
(4/5)
715.7
1.3e3
(0/1)
10.8
Qwcn2,5-VL-7B
445.3
53.7
1.8c4
(1/2)
2.0c4
(1/5)
5.6c3
(3/4)
(0/1)
14.4
Time Series Models
Moirai-Large
(0/1)
(0/1)
51.7
(1/2)
(3/5)
(1/2)
(3/4)
360.1
8.3
LimeMoE-Large
(0/1)
(0/1)
39.0
(1/2)
(1/2)
(3/5)
(1/2)
(3/4)
1.2c3
9.1
Chronos-bolt-Be Base
(0/1)
(0/1)
41.5
(1/2)
(1/2)
(3/5)
(1/2)
(3/4)
440.4
UniTS
3.3c6
(0/1)
42.0
(1/2)
147.3
(4/5)
216.3
(3/4)
(0/1)
9.8
TimeOmni(Qurs)
2.8
2.2
37.5
5.3
46.6
66.4
91.7
402.7
656.5
4.1
6.3 GENERATION TASKS
Table compares various models generation tasks. Overall, generation tasks prove more chal- lenging, as reflected by the large SwMAPE values reported in the table. Unlike understanding tasks, representing time series text generally outperforms image-based inputs for generation tasks where precise numerical values are critical for accurate generation, as expected. Gemini2.5 performs best in neuroscience, physiology, and urbanism, where the output time series relatively short (typ-
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ically fewer than 20 steps For long-term forecasting tasks, such Task ENG02 with output lengths up to 720, all LLMs struggle to generate sequences of the required length, with success rates commonly below 10% (Table 12). Closed-soure LLMs continue to outperform open-source coun- terparts across most domains. Many values for unified forecasting models are missing in the table, they cannot handle imputation event localisation tasks. However, they achieve superior perfor- mance on supported forecasting tasks, with Moirai giving the overall lowest average SwMAPE in economics and mathematics. The proposed TimeOmni model achieves the highest overall ranking, closely followed by Gemini2.5-Flan using text input. Detailed results for each task are provided in Appendix
6.4 MODEL RANKINGS ACROSS DISCIPLINES
Fig. 5 shows the average rankings
of 17 tested models across 12 disci- plines. We rank models for each of the 43 tasks and average the rank- ing over tasks within each disci- pline. The benchmarking results re- veal several clear patterns. General- purpose LLMs such as GPT-4.1-mini, Gemini2.5-Flash. DeepSeek-V. V3. and GPT-5-mini deliver strong all-around results, usually falling within the top 5 to 8 ranks. Multimodal LLMs like Vn and QwenVL occupy a middle ground, showing compet- itive performance in sensory-heavy fields such bioacoustics, earth sci- ence, and radar, but weaker results in other areas. Specialised time-series models, including Moirai, TimeMoF, Chronos, and ChatTS, struggle with generalisation. They often collapse to the bottom of the ranking across most domains due to limitations of supported task types, but occasionally excel in specific cases, such Moirai and Chronos in math- ematics and ChatTS in earth science. Discipline-specific patterns also emerge: models consistently perform well across language-heavy domains such Economies, while dedicated time-series mod- els show strengths in more structured or periodic domains like Energy and Meteorology. but degrade sharply elsewhere. As illustrative framework. TimeOmni achieves consistently top performance across nearly all disciplines, with slightly lower in mathematics and urbanism.
HPPT-41-mint
Gemini2. S-Flash
DeepSeck-V3
Llama3-8B
Qwen3-8B
Qwen3-4B
12
GPT-5-mini
Seemini2.5-Flasaae
10
10
10
InxemVL3.5-8B
16
12
IntemmV.3.554B
10
-8
Qwen2.5-VL.
Moirui-Large
TimeMOE-Large
Chronos--olt-BBag
Cha:TS
14
11
16
18
UniTS
11
11
TimeOmni(ours)
Astron
Bioncous
EartiSei.
Eccm.
Energy
Math.
Metero.
Manuf
Neura.
Phyato.
Radar
Urhan.
AvgRk
Figure 5: Average model rankings across disciplines, com- puted as the mean rank over all domain tasks within each discipline, rounded for visualisation purposes.
These results suggest two key insights. First, time series should be treated distinct modality rather than being directly converted to text or images, as such transformations lead to information loss and limit model performance. Second, general-purpose LLMs demonstrate better generalisa- tion to unseen domains compared with specialised time-series models, highlighting the benefit of combining LLMs with dedicated time series processing to effectively handle diverse scientific data. TimeOmni provides illustrative step that incorporates time series into LLMs while remaining compatible with general L.M training.
6.5 DESIGN ANALYSIS: WHAT IS NEEDED FOR SCIENTIFIC TIME SERIES MODELLING?
To better understand what is essential for modelling scientific time series, we first conducted abla- tion experiments on the reprogramming module and patch expert routing mechanism TimeOmni. Due to space constraints, summarise main findings here and defer complete experimental results to Appendix G Replacing the reprogramming module with a simple MLP consistently degrades performance, suggesting that explicit reprogramming is important for aligning time series represen-
Detailed statisties of each task be found in Table

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Published as a conference paper at ICLR 2026
tations with the LLM and improving robustness across tasks. Replacing patch expert routing with fixed patch size faces a fundamental dilemma: small patch sizes become infeasible for very long se- quences due memory constraints and difficulty of modelling extremely Long-range dependencies, while large patch sizes collapse short sequences into a single patch. Choosing an intermediate patch size leads to clear performance drops sequences with extreme temporal scales, highlighting the need for scale-adaptive patching in scientific time series.
To complement the above analysis, we further investigate whether existing models be directly adapted to scientific time series through finetuning. We additionally finetuned one image-text LLM 5VL77) and one time-series foundation model (TimeMoE) using the same training data as TimeOmni. Finetuning Qwen2.5VL improves follloiinn ability on some tasks, but the gains are limited possibly because representing time series as images compromises numerical precision. We also observed instability during finetuning and difficulty in maintaining balanced per- formance across tasks, potentially reflecting the limited capacity of image-based representations for time-series data. We then finetune TimeMoE, improves on some forecasting tasks, finetuning does not enable it to handle tasks it could not pre- viously address, such imputation or QA. In sum, results provide evidence that challenges arises from architectural limitations and cannot be overcome through finetuning alone. Full results and discussion provided in Appendix H
cerrastiin-ooieente model. While performance
7 CONCLUSIONS
We introduce SciTS, benchmark for scientific time series understanding and generation, spanning 12 disciplines, 7 task types, 43 domain-specific tasks, and over 50k instances, with a wide range of frequencies, lengths, and multivariate dimensions. We benchmark 17 models on SciTS, including text-only LLMs, multimodal LLMs, and unified time series models. Two key insights emerge. First, LLMs exhibit stronger adaptability and generalisation compared with models specialised for time series. Second, while general- dedicated temporal processing crucial handle complex scientific dynamics. Serialising tempo- ral signals as text rasterising them as images incurs information loss and hinders efficiency and numerical fidelity. To this end, we provide TimeOmni, a demonstrated framework that integrates time series into LLMs while remaining compatible with general-purpose LLM training. By provid- ing a comprehensive benchmark and a preliminary approach, this work takes a step toward LLMs that faithfully interrogate dynamical systems, enable discovery, and guide experimental design on real-world scientific temporal data.
general-purpose purpose LLMs generalise better to unseen domains, pairing them with
ACKNOWLEDGMENTS
Supported by Shanghai Artificial Intelligence Laboratory. We thank the PrismaX Team support and valuable advice in building SciTS.
f for their
htttpss//hugggingffcc..oo smax
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LIMITATIONS AND ETHICS STATEMENT
Due to resource constraints, we evaluate all baselines in a zero-shot setting without finetuning. More explanation of the evaluation setting can be found in Appendix H In addition, our preliminary small-scale experiments indicate that enabling the "thinking" mode in closed-source LLMs does not improve performance on time-series tasks. Since also incurs substantial computational cost, disable this option for model assessments.
This work focuses on benchmarking and modelling methods for scientific time series. No personally identifiable or sensitive human data are included. As with most research in machine learning, new modelling techniques could potentially be misused by malicious actors; however, we do not believe that TimeOmni poses risks beyond those associated with existing methods.
REPRODUCIBILITY STATEMENT
Additional statistics and details of SciTS are provided in Appendix D with case examples in Ap- pendix ! detailed description of the assessed models given Appendix [ and implemen- tation details of TimeOmni are presented in Appendix [ hettps://huggingface.. available at http
The SciTS benchmark is available at SciT5
The TimeOmni framework
com wwom/OppenTSLaae Time:/eSSaaa/immmmiiim
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A
THE USE OF LARGE LANGUAGE MODELS (LLMS)
LLMs were employed solely to polish the writing and correct grammatical errors.
B EXTENDED RELATED WORK
B.1 TIME SERIES MODELS
Traditional Time Series Models. Traditional task-specific models are typically trained in su- pervised manner on individual datasets, requiring carefully designed architectures and specialized output modules tailored to specific tasks. For classification tasks such as anomaly detection, regime identification, or event recognition, models commonly employ final classification head whose structure depends directly on the number of predefined classes in the target dataset (Lu et al. [023] Liu et al. 2023: Ding et al., 2023). This design choice limits their generalization ability, as the model must be reconfigured and retrained whenever applied to a new classification scenario with different class cardinality. Similarly, time series forecasting and imputation models often de- veloped and optimized in isolation. Architectures such as i'Tranformmer (Liu et al. 2024d), which reformulate Transformer-based modelling through inverted attention for improved long-term pre- diction, and TimeMixer (Wang et al. 2024a),, which introduces hybrid temporal projections for efficient multi-scale forecasting, are typically trained on narrowly defined forecasting benchmarks. Likewise, imputation methods like Kim et al. 2023). Tactis-2 (Ashok et al., 2024) are usually eval- uated only on missing-daie reconstruction tasks using domain-specifee datasets. As a result, these models lack cross-task compatibility and fail to develop unified understanding of temporal patterns across diverse modalities and applications. This fragmentation leads to inefficiencies deployment, scalability, and maintenance, especially when multiple time series tasks need to be performed simul- tancously in real-world systems.
Unified Time Series Models. Moirai (Woo et al. 2024) propose the Masked Encoder-based Univer- sal Forecasting Transformer architecture, capable of processing various time series signals. Models building this, Moirai-MoE (Xu et al.) 2025a) and Time-MoE (Shi et al. (025) integrate Mixture of Experts (MoE) into the Transformer framework, enabling specialized subnetworks to capture het- erogeneous temporal patterns through dynamic routing. Concurrently, TimeMixer++ (Wang al.) 20255) advances the paradigm by capturing task-adaptive temporal patterns through a multi-scale decomposition strategy, simultancously modelling intricate dependencies in both the time and fre- quency domains. This dual-domain analysis allows the model to extract features at varying temporal resolutions, enhancing its ability to generalize across tasks with differing periodicities and noise pro- files. In contrast to architectural overhauls of large Transformer backbones, Simple'TM (Chen et al., 2025a) adopts a more principled and minimalist design philosophy. It integrates classical signal processing concepts-such as local averaging and trend-seasonal decomposition-direttt lightweight neural architecture, complemented by a carefully modified attention mechanism. Rather than introducing complex modifications to pre-existing farge-scale model, SimpleTTM incremen- tally incorporates domain- innpiine components from scratch, achieving competitive performance with significantly reduced model complexity. Addressing the pervasive challenge of missing data in real-world time series, S4M (Peng et al.) 2025) introduces a prototype- -based memory mechanism that leverages rich historical patterns stored in a learnable prototype bank. By encoding both ob- served values and binary masks indicating missingness, the model learns robust and context-aware representations that are resilient to data incompleteness. The integration of explicit missingness indicators with structured memory enables more accurate imputation and forecasting.
into
LLMs for Time Series Modelling. Recent studies have critically examined the efficacy of large language models (LLMs) in time series forecasting and reasoning. Tan et al. (2024) argue that LLM- based time series forecasters perform on par with-or or even worse than than-basie L.-M-free ablations, suggesting that the pretrained knowledge in LLMs may not be effectively leveraged for temporal prediction. ZZhou& & Yu (2025)'s comprehensive analysis of LLMs' understanding of time series using anomaly detection also suggests that while LLMs can identify simple anomalies, their ca- pacity for numerical reasoning and deep temporal comprehension remains severely constrained. In response, several works propose multimodal alignment-basee based frameworks to better interface time series data with LLMs. ChatTS (Xie et al. (025) introduces a novel multimodal LLM architecture that treats time series as first-class modality akin to images in lanion-l models, enabling
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end-to-end understanding and reasoning over temporal data. Another line of work focuses on mantic and structural alignment. Hu et al. (2025) propose a paradigm that aligns time series data with linguistic representations within the familiar context space of LLMs, leveraging the models' inherent strengths in logical and structural reasoning to improve temporal comprehension. Building on this idea, TempoGPT (Zhang et al., 2025) quantizes temporal embeddings into discrete tokens and expands the LLM's embedding layer to jointly process temporal and textual inputs, achieving unified representation space. Similarly, TIME-LLM (Jin al., 2024) presents a framework to adapt LLMs for time series forecasting without modifying the pretrained backbone. It reprograms raw time series into text-like prototype representations, enriching them with contextual descriptions and task instructions in natural language, thereby aligning temporal data with the LLM's linguistic inductive biases.
B.2 SCIENCE BENCHMARKS AND TIME SERIES BENCHMARKS
Science Data Benchmarks. Scientists' First Exam (SFE) Zhou et al. 2025 comprises 830 expert-validated Visual Question Answering (VQA) pairs spanning five high-value scientific dis- aoplines-astronnn chemistry, earth science, life science, and materials science and incorporat- ing 17 distinct scientific data formats, such as spectra, phase diagrams, and remote sensing images. ScienceQA (Lu et al. 2022) focuses on reasoning capabilities over educational general-purpose data visualizations. It comprises approximately 21, 000 multimodal (texr, image) multiple-choice questions, which feature explanations in Ohhiuuof-Thought (Wei et al., 2022) format to simulate multi-hop reasoning processes. ChartQA (Masry et al.] (022). along with its subsequent version CHARTQAPRO (Masry et al. 2025), concentrates on VQA for charts, necessitating visual and log- ical reasoning capabilities from the models. Notably, CHARTQAPRO expands data diversity by introducing 1, 341 charts from 99 different sources, covering complex formats like infographics and dashboards, while also integrating question types designed to be more challenging. However, an in-depth analysis these image input benchmarks uncovers "visualization gap. This circumvents the entire data processing and analysis stage.
Time Series Benchmarks. Existing time series benchmarks mainly provide contextual informa- tion for the data by integrating time series with text. TimeSeriesExam (Cai et al. 2024) contains 700 questions generated from 104 carefully curated templates and provides a set of configurable tasks designed to evaluate a model's understanding of core concepts such causality, patterns, and noise. MTBench (Chen et al.] 2025b) aligns financial news and weather reports with their corresponding stock price and temperature time series, which supports tasks such as news-driven question answering and trend analysis tasks. Time-MMD (Liu et al. 2024b) further extends by offering the first large-scale, multi-domain dataset, which covers major sectors (such as agricul- ture, energy, and healthcare) and enables fine-grained alignment between numerical and textual sequences. Time-MQA introduces a unified, large-scale question answering framework, including approximately 200, 000 questions across more than 12 domains. However, synthetic data bench- marks (e.g., TimeSeriesEXam) provide high controllability and interpretability, yet this approach may fall short of generalizing to real-world data. Existing real-world data benchmarks (e.g.. Time- MQA) offer authenticity but may lack the systematic coverage of synthetic methods. Our SciTS achieves balance of both: it is sourced from 11 scientific domains and 40 tasks, ensuring mod- els are exposed to a broad spectrum of real-world phenomena and also apply synthetic data for a systematic consideration.
C IMPLEMENTATION DETAILS OF TIMEOMNI
We initialise TimeOmni with the pretrained weights of Qwen3-8B (Yang et al. 2025a) and finetune it using DoRA (Liu et al. 2024c) with rank = 8 and dora_alpha = 32. The model is optimised using the Adam optimizer with a learning rate of 2 x 10 5 A linear warmup is applied for the first 5% of the training steps, followed by a cosine learning rate decay schedule. The model is trained for total of 10 epochs. In each training step, both understanding and generation tasks were optimised concurrently, with batch size of 6 for understanding tasks and for generation tasks. Experiments were conducted on NVIDIA H20-140G GPUs, utilising the DeepSpeed ZeRO stage optimisation and 6 precision for training.
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For understanding tasks, the input time series is directly flattened from T to RT For generation tasks, the preprocessing strategy depends on the number of channels in the output signal. If the imput signal is multi-channel but the output signal contains only single channel, the imput flattened in the same manner as for understanding tasks. If the input and output signals have the same number of channels, prediction is performed per-channel basis without flattening.
D MORE DETAILS OF SCITS
This section provides more details and statistics for the SciTS benchmark. Table lists the number of instances, range of length, frequency and number of channels for each discipline.
Table 5: Discipline-level statistics of SciTS.
Discipline
Instances
Min Len.
Max Len.
Min Freq. (Hz)
Max Freq. (Hz)
Dim. Range
Astronomy
2583
24
6.4c4
1.2e-5
1.6c4
1
Earth Science
3080
6.0e3
6.0c3
1.0e2
1.0e2
Bioacoustics
4976
2.9c3
1.1c7
2.2c4
9.6e4
1
Meteorology
3976
3.4c2
2.8c-4
2.8c-4
1~5
Economics
1946
50
7.6e2
1.2e-5
1.le-3
1~5
Neuroscience
7146
68
4.0e3
1,0e2
5.0e3
1~-58
Energy
8501
48
2.6e2
2.8e-4
2.5e-1
Physiology
9824
9
5.0c3
1.3e2
5.0c2
1~112
Urhanism
3492
14
2.6e2
2.8e-4
2.8c-4
1
Manufacturing
4400
1.2e3
1.6e5
1.2c4
1.6e4
I
Radar
2243
33
3.2e2
3,2e6
1.0e7
2
Math
1856
1.0e2
1.0c2
1.0c2
1.0e2
3
D.1
TASKS DESCRIPTION
Table 6 presents the tasks associated with each discipline, along with their IDs and types. We give detailed description of each task below.
Astronomy. In the field of astronomy, researchers analyse celestial phenomena using diverse data
sources. Three tasks are involved:
-aavit-ravitatiinall wave anomaly detection: Identifying unusual patterns in detector data that may indicate gravitational wave signals among noise and instrumental artifacts. The imput to this task consists of time series data and text, and the output is text.
ASG02-Gravitatione wave event localisation: Verifying candidate signals and determine the occurrence time of gravitational waves. The input to this task consists of time series data and text, and the output text.
Categorising celestial objects by their brightness varia- tions over time to identify phenomena such as supernovae, variable stars, and exoplanetary transits. The input for this task includes time series data and text, with the output being text.
ASU03-Light curve classification:
Earth Science. In the field of Earth science, researchers analyse seismic waveform data from global networks to monitor and understand subsurface activities. Two tasks are involved:
EAU01-Earte Qurt--kat anomaly detection: Identifying unusual signals in seismic data that do not conform to normal noise typical earthquake waveform patterns, which may indi- cate abnormal geophysical processes or potential disaster precursors. The input to this task consists of three-channel time series data and text, and the output is text.
E.AGO-Earrthqaale event localisation: Automatically identifying and locate signals pro- duced by real earthquakes, such as waves and S waves, in continuous seismic records, and accurately determine their origin time, hypocentral location, and magnitude. The input for this task includes three-channel time series data and text, with the output being text.
Bioacoustics. In the field of bioacoustics, scientists monitor and study wildlife by collecting and analysing sound data from the environment. Three tasks involved:
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Table 6: Task overview of SciTS.
Discipline
Task Brief
Task ID
Task Type
Gravitational wave anomaly detection
ASU01
Anomaly detection
Astronomy
Gravitational wave event localisation
ASG02
Event localisation
Light curve classification
ASU03
Classification
Earthquake anomaly detection
EAU01
Anomaly detection
Earth Science
Earthquake event localisation
EAG02
Event localisation
Birds vocalisation classification Animal vocalisation classification
BIU01
Classification
Bioacoustics
BIU02
Classification
Marmoset vocalisation classification
BIU03
Classification
Weather anomaly event dctection
MEU01
Anomaly detection
Rainfall anomaly event detection Temperature forecasting Temperature MCQ
MEU02
Anomaly detection
Meteorology
MEG03
Forecasting
MEU04 MCQ
Stock closing price forecasting
ECG01
Forecasting
Economics
Stock price forecasting
RCG02
Forecasting
Stock MCQ
ECU03
MCQ
Depressive disorder detection
NEU01
Anomaly detection
EEG pattern classification
NEU02
Classification
EEG signal forecasting
NEG03
Forccasting
Neuroscience
EEG signal imputation
NEG04 Imputation
Motor imagery classification
NEUOS
Classification
Sleep staging classification
NEU06
Classification
Generate sensor signal trend hased description
ENG01
Synthesis
Transformer sensor signal forecasting
ENG02
Forecasting
Energy
Electricity load forecasting
ENG03
Forecasting
Comprehensive electricity forecasting
ENG04
Forecasting
Comprehensive electricity imputation
ENG05
Imputation
ECG status classification Physiological signal forecasting
PHU01
Classification
PHG02
Forecasting
Physiological signal imputation
PHG03
Imputation
Physiology
ECG anomaly detection
PHU04
Anomaly detection
Gait freezing anomaly detection
PHU05
Anomaly detection
Human activity classification
PHU06
Classification
Traffic flow forecasting
URG01
Forecasting
Pedestrian flow forecasting
URG02
Forccasting
Urbanism
Pedestrian flow imputation
URG03 Imputation
Traffic flow anomaly detection
URU04
Anomaly detection
Traffic volume forecasting
URGOS
Forecasting
Bearings fault location detection from vibration signals
MFU01
Classification
Manufacturing
Bearings fault size detection from vibration signals
MFU02
Classification
Machine malfunction detection from operational recordings
MFU03
Anomaly detection
Coding scheme classification
RAU01
Classification
Radar
Modes and modulation classification
RAU02
Classification
Math
Evolution of chaotic system forecasting
MAG01
Forecasting
Identifying bird species present in audio record- ings, typically by analysing their distinctive calls or songs. The input to this task consists of time series data and text, and the output is text.
BIUO1-Birds vocalisation classification:
BIU02-Animan vocalisation classification: Recognising sounds from a broader range of animal groups, such mammals, insects, and amphibians. The input this task consists of time series data and text, and the output is text.
Specialized in classifying the vocalizations of marmosets, type of small New World monkey, often used to study their complex social communication. The input to this task consists of time series data and text, and the output is text.
BIU03-Marmosef vocalisation classification:
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Meteorology. In the field of meteorology, researchers analyse multi-source meteorological data to understand and predict atmospheric phenomena. Four tasks are involved:
MEUU0-Weather anomaly event detection: Identifying extreme weather events that de- viate from normal climate patterns, such cold waves, heatwaves, persistent heavy rainfall. The input to this task consists time series data and text, and the output text.
MEU002RRainan anomaly event detection: Detecting rainfall patterns that significantly depart from historical norms, supporting flood early warning and water resource manage- ment. The input to this task consists of five-channel time series data and text, and the output is text.
MEG03-TB DEGGO--Tempeature forecasting: Forecasting temperature changes over specific future time periods, forming the basis for daily and agricultural meteorological services. The input to this task consists of single-channel time series data and text, and the output is single-channel time series data with a maximum length of 72.
MCQ: Systematically analysing temperature data, identify trends potential anomalies in the data, and provide basis for subsequent decision-making. The input to this task consists of time series data and text, and the output is text.
Mee004-Tmpeeaan
Economics. In the field of economics and finance, researchers and practitioners analyse market data predict price movements and ensure data quality. Three tasks are involved:
ECG01-Stock closing price forecasting: Predicting the final price of stock the end of specific trading day, a key reference for investment decisions. The input to this task consists of five-channel time series data and text, and the output single-channel time series data.
ECG02-Stock price forecasting: Predicting the stock price its movement over future time horizon. The input to this task consists of single-channe time series data and text, and the output is single-channel time series data.
ECU03-Stocc MCQ: Systematically analysing stock market data to identify and rectify erroneous or anomalous records, ensuring data accuracy and reliability. The input for this task includes time series data and text, with the output being text.
Neuroscience. In the field of neuroscience, researchers analyse electroenceehalogram (EEG) sig- nals to understand brain function, diagnose neurological disorders, and develop brain-computer in- terfaces. Six tasks are involved:
Detecting changes in brain activity related to de- pressive episodes by analysing the characteristic patterns of EEG signals. The input to this task consists of multichannel time series data and text, and the output is text.
NEU01-Depressive disorder detection:
NEU02-FEG pattern classification: Recognising different types of waveforms in EEG signals, commonly used in the diagnosis of epilepsy and other disorders. The input to this task consists of multichannel time series data and text, and the output is text.
NEG03-EEG signal forecasting: Forecasting the EEG signals of various patients, includ- ing amyotrophic lateral sclerosis (ALS), major depressive disorder (MDD), ADHD, and OCD, can help understand disease progression and evaluate treatment outcomes. The input to this task consists of single-channe time series data and text, and the output is single- channel time series data with a maximum length of 32.
NEG04-EEG signal imputation: Filling in missing data caused by artifacts or equipment issues in EEG recordings of various patients, including amyotrophic lateral sclerosis (ALS), major depressive disorder (MDD), ADHD, and OCD, to ensure data integrity. The input this task consists of single-channel time series data and text, and the output single- channel time series data with a maximum length of 12.
NEU05-Motor imagery classification: Recognising unique brain patterns generated by different movement intentions. The input to this task consists mulli--hanner time series data and text, and the output is text.
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NEU06-Sleep staging classification: Automatically classifying sleep into different stages based on EEG signal characteristics, used for sleep quality assessment and sleep disorder diagnosis. The input to this task consists of single-channel time series data and text, and the output is text.
Energy. In the field of energy, researchers analyse various types of energy data optimize grid dispatch, improve energy efficiency, and ensure system stability. Five tasks involved:
ENGoI-Gemerate sensor signal trend based on description: Using ETT (Electricity Transformer Temperature) description to forecast the temperature of power transformers in the future in reverse, for analysing temperature trends and diagnosing anomalies. The imput to this task is text, and the output is single-channel time series data with maximum length of 96.
SNNGO-Trrasformer sensor signal forecasting: Forecasting the future temperature of power transformers, which is crucial for preventing equipment overheating, scheduling maintenance, and ensuring the safe operation of the power grid. The input to this task consists of single-channel time series data and text, and the output is single-channel time series data with a length of 96 for short-term forecasting and 720 for long-term forecasting.
INNGO3-Electriiit load forecasting: Forecasting future electricity demand (load), which is fundamental to power system planning, dispatching, and energy trading. The input to this task consists of single-channel time series data and text, and the output single-channel time series data with a maximum length of 48.
CoGG--Compreeennive electricity forecasting: Forecasting future changes in electricity based on the demand, generation, consumption of electricity in certain city, and support the fine planning of the city's 's energy. The input to this task consists of single-channel time series data and text, and the output single-channel time series data with maximum length of 32.
electricity imputation: Filling in missing values in electricity demand, production, consumption data caused by communication failures equipment issues to ensure data integrity and achieve accurate analysis and prediction. The input this task consists of single-channel time series data and text, and the output single- channel time series data with a maximum length of 12.
SANGO5-Comprehensee
Physiology. the field of physiology, researchers analyse human physiological signals and health data to study bodily functions, monitor health status, and assist in disease management. Six tasks are involved:
PHU01-ECG status classification: Classifying heart rhythm types abnormal cardiac states by analysing electrical activity waveforms to assist in the diagnosis of cardiovascular diseases. The input to this task consists of 12-channel time series data and text, and the output is text.
Forecasting future changes in physiological sta- tus based on historical health indicators, providing basis for personalized health manage- ment and disease prevention. The input to this task consists single-channel time series data and text, and the output is single-channel time series data with a maximum length of 32.
Physoo-hyyiological signal forecasting:
PHGoo--hyyiological signal imputation: Filling missing values in health monitoring data caused by device failures monitoring interruptions to ensure data integrity for accu- rate analysis. The input to this task consists of single-channel time series data and text, and the output is single-channel time series data with maximum length of 12.
PHU04-ECG anomaly detection: Identifying abnormal segments that deviate from nor- mal patterns in ECG signals, enabling timely warning of potential heart issues. The input to this task consists of single-channel time series data and text, and the output is text.
PHU05-Gait freezing anomaly detection: Detecting sudden "freezing" episodes (tempo- rary movement cessation) during walking in patients with Parkinson's 's disease, supporting rehabilitation assessment and intervention. The input to this task consists of single-channel time series data and text, and the output is text.
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PHU06-Human activity classification: Recognising types of daily human activities (e.g., walking, sitting, climbing stairs) using data from accelerometers and gyroscopes, for move- ment monitoring and health evaluation. The input to this task consists of single-channel time series data and text, and the output is text.
Urbanism. In the field of urbanism, researchers analyse traffic and pedestrian data to optimize urban planning. improve travel efficiency, and ensure public safety. Five tasks involved:
URGOI-Traffic flow forecasting: Forecasting future changes in vehicle flow road net- works during specific time periods, serving as the core basis for traffic management and signal control. The input to this task consists of single-channel time series data and text, and the output is single-channel time series data with a maximum length of 24.
URGO2-Pedestrian flow forecasting: Forecasting future pedestrian flow and distribution in specific areas (such as around business districts, subway stations, and large venues) to support public safety management and business decisions. The input to this task consists of single-channel time series data and text, and the output is single-channel time series data with maximum length of 32.
Peneereeen flow imputation: Filling in missing values pedestrian monitoring data caused by equipment failures, network interruptions, or other issues to ensure the accuracy and continuity of data analysis. The input to this task consists of single-channel time series data and text, and the output is single-channel time series data.
URU04-Traffie flow anomaly detection: Identifying abnormal conditions that deviate from normal patterns in real-time traffic data, such traffic jams, accidents, road clo- sures, to enable timely warning and guidance. The input to this task consists of single- channel time series data and text, and the output is text.
URGOS-Traffe volume forecasting: Forecasting the total number of vehicles that will pass through a specific road segment or intersection in the future, which is fundamental to transportation planning, infrastructure construction, and traffic demand management. The input to this task consists of single-channel time series data and text, and the output is single-channel time series data with a maximum length of
Manufacturing. In the field of manufacturing, researchers and engineers analyse industrial sensor data to enable equipment health management and intelligent maintenance. Two tasks are involved:
MIUU0--Bearig fault location detection from vibration signals: Classifying bearings health status or fault types by analysing signals such as bearing vibrations, which a key technology for predictive maintenance. The input to this task consists of single-channel time series data and text, and the output is text.
MFU02- MFUO2-Bearings f'ault size detection from vibration signals Detecting the size of fault through the analysis of vibration signals from industrial bearings (including machine tools, pumps, and fans) helps identify abnormal states deviating from normal modes. The input to this task consists of single-channel time series data and text, and the output is text.
MFU03-Maching malfunction detection from operational recording: Identifying ab- normal patterns (which may indicate potential equipment failures) automatically relies on analysing various data generated while industrial equipment is in operation. The input to this task consists of single-channel time series data and text, and the output text.
Radar. In the field of radar, researchers analyse radar echo signals to achieve target detection, recognition, and environmental perception. Two tasks are involved:
RAU01-Coding scheme classification: Classifying radar data segments to identify ground targets or terrain types. The input this task consists of two-channel time series data and text, and the output is text.
RAU02-Modes and modulation classification: Classifying raw processed signals ceived by radar to identify their modulation type, source, or target attributes. The input to this task consists of two-channel time series data and text, and the output is text.
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Table 7: Task-level statistics of SciTS.
Discipline
Task IT) Instances
Input Length Range Length Range
Generation TS
Frequency Range(Hz))
Channel Range
Data Source
ASUOI
1024
6.404
1.6c4
Simulation
Astronomy
ASG02
535
6,404
1.644
Simulation
24-1.3e3
1.2e-5
Earth Science
EAU01
2048
6.0c3
1.0c2
3
STEAD
Mousavi
2019
EAG02
1032
6.0e3
1.0e2
STEAD
Niuuaai ct
2019
BTD01
1602
2.9c3-4.2o6
3.2e4
Powdermill
Chronister
202
Bioacoustics
BIU02
2474
1.704~11.17
2.2c4
1
iNaturalist
hasma et 01 2024
BTUOS
900
6,204-2.445
9.6c4
MarmAudio
MEU01
1256
16
Unknown
1
TS MQA
Kone et al 2025
TIMECAP
Meccorloog
ME000
1176
1.7e2~x3.4e2
24-/22
2.8e-4
MIIbench
Usen et a
20236
1.7e2-33442
MTbeenh
20255
ECG01
1260
50
3
1.2c-5
TimMultiTime
XX
20256
Economics
1.222-7.622
11-20e2
1.le33
MTTeech Chen
202A1
ECU03
303
1.162~4.0c2
1.1e-3
MTbench Chenn et 2025
NEUOI
1000
2.5e3
2.6e2
18-20
MDD Mumntaz 2016
NEU02
789
2.562~44.03
2.5c2
23
TUEV (Obera Picone 2016
Neuroscience
NEG03
360
68-22.522
8-332
2.6e2 5.0e2
IS MQX (Kong (Kong
al: 2022
NEG04
373
97-226e2
4--12
2.6e2 ~ 5.0e2
TS MQA Konng
2022
NEUOS
3600
1.03
2.5e2
WBCIC SHU Yane cl 20255
NEU06
1024
3.0c3
1.0e2
Sleep Keehpp
20.00
ENGOL
2700
/
24..96
2.8e
textET Ge
20255
ENG02
5570
96
96-7.222
2.8c-4
ETT Wi
Energy
ENG03
100
48
5.6e 4
NewsFooreca
Wang milm
20224b
ENG04
66
1.0c2~2,5e2
8--32
2.8e-4 tu 2.5e=1
TS MQA
Konz
2022
ENGOS
65
2.6e2
4--12
2.5e:1 2.8e
MQA
Kong al
2025
PHUOI
2090
5.0c3
5.0c2
PTB XL
Wagner
2020
PHGM2
1517
67-2.5522
8-322
1.3e2 ~ 2.6e2
TS MQA
Kong et m 2025
Physiology
PHG03
1511
97-226c2
4--12
1.302 2.6c2
MQA
PHUN4
1006
64
5.0e2
TS MQA
Kong m
202.5
PHU05
64
MQA
PHU06
1818
30
20
TS MQA
Kong
4025
URG01
2.8c-4
NewsForecast
WWang
20246
URG02
62
1.152-22.5e2
8--32
2.8e-4
TS MQA
Kong a
2125
Urbanism
URG03
1.362~2..622
2.8c-4
MQA
URU04
122
16
Unknown
TS MQA
Kong 3
21255
URG05
3200
14<21
3-/7
2.8c-4
MetroTraflic
Wang
MFU01
400
1.2e3--488e1
1.2c4
CWRU Center Center
Manufacturing
1,283-4,8c3
1.264
MFU00
3600
1 1.6e5
1.644
MIMI Due Tunabe alall
2021
Radar
RAU01
963
35--3.205
3.2c6
RadSee (Huane et 2024
RAU02
1280
1.3e2
1.0e7
RadarComm Jagamath Jegunnath
2021
Math
MAGOI
1856
1.0e2
20
1.0e2
3
Chaotic Gilpin 2023
Mathematics. In the field of mathematics, researchers analyse the behavior of chaotic systems to understand their dynamic properties and make predictions. One task involved:
MAG01-Evolution of chaotic system forecasting: Performing short-term predictions for chaotic systems that are driven by underlying ordinary differential equations (ODEs) gov- erning their dynamics (such the Lorenz system). These systems exhibit extreme sensi- tivity to initial conditions and complex nonlinear behaviors. The input to this task consists of single-channel time series data and text, and the output is text with length of 20.
Table presents task-level statistics, including the ranges of input and target time series lengths (where applicable) and the corresponding data sources.
D.2 DATASET DESCRIPTION
This section provides description of the datasets contained in SciTS.
GWOSC GW Event. Since the number of observed gravitational events is quite limited. We con- structed GWOSC GW Event dataset by simulation, following prior work in astronomy (Dax et al. 2021 2023 2025). To construct synthetic data that closely match real observations, we generated gravitational- wave templates via numerical relativity simulations and injected them into strain data
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downloaded from the GWOSC platform Unlike the traditional approach of generating noise from a fixed power spectral density (PSD), this method leverages real detector noise segments to enrich noise diversity. Specifically, we applied data-quality and injection masks to exclude low-quality or contaminated intervals, and extracted 4-second noise segments (sampled at 16 kHz) starting 16 s after each event form noise pool. A template bank was built through grid search over typi- cal binary black hole mass ranges. For each template, two noise segments were randomly drawn and scaled different matched-filter SNRs by adjusting the amplitude ratio. To mitigate trunca- tion artifacts, windowing was applied during waveform-noiss superposition, and the final data were whitened to produce realistic and training- ready synthetic datasets.
LEAVES (Fei et al. 2024) is large-scale light-curve dataset designed for the automatic classifica- tion of variable stars. It constructed by merging open data from three major astronomical surveys: the All-Sky Automated Survey for SuperNovae (ASAS-SN), Gaia, and the Zwicky Transient Facility (ZTF). The dataset comprises 977,953 light curves from variable stars and 134,592 from nonvari- able sources. All variable stars are annotated with seven superclasses: Eclipsing binaries, Rotational variables, RR Lyrae, Cepheids, Long-period variables, Delta Scuti, and Nonvariable.
STEAD (Mousvi et al. 2019) is large-scale global dataset containing 1.2M labelled waveforms, 19k hours of data, and 450k earthquakes that occurred between January 1984 and August 2018. All waveforms from the dataset have three channels, each of 1-minute length with sampling rate of 100Hz. We process the three-channel data by calculating its Euclidean norm, which transforms the data into single-channel waveform representing the total amplitude of the ground motion.
Powdermill (Chronister et al. 2021) is the first strongly labelled bird soundscape recording dataset (with information on timing, frequency, and species). Collected the Powdermill Nature Reserve in Pennsylvania, northeastern United States, the dataset includes dawn chorus recordings captured by acoustic recorders between April and July 2018. Continuous recordings were made over four days during the study period, covering 48 species and 16,052 annotations.
iNaturalist Sounds dataset (iNatSounds) (Chasmai 230,000 audio recordings capturing sounds from over 5,500 species, including birds, mammals, insects, reptiles, and amphibians. Each recording in the dataset varies in length and includes a single species annotation.
2024) is large-scale collection of
MarmAudio (Lamothe et al.) 2025) a large-scale dataset of common marmoset vocalizations, recorded over 40 months at high sampling rate of 96 kHz in a soundproofed animal facility room containing three cages (s:20 marmosets). The dataset comprises more than 800,000 short audio segments (=253 hours), capturing the full vocal repertoire observed during the recording period. A subset of approximately 215,000 calls (=72 hours) is annotated with six vocalization InfantInfant cry, Phee, Seep, Trill, Tsik, and Twitter providing rich supervision for downstream analysis.
Time MQA (Kong et al., 2025) is a large-scale dataset designed for time series multi-task question answering, constructed from wide range of publicly available time series benchmarks. It spans over twelve domains, including healthcare, finance, and energy. The dataset comprises approximately 200,000 question-answer pairs, covering five tasks: forecasting, imputation, anomaly detection, classification, and open-ended reasoning. Each entry is enhanced with contextual text, such as background information and feature descriptions, to provide rich supervision for both numerical analysis and advanced reasoning tasks.
TimeCAP (Lee et al., 2025) dataset in the weather domain is sub-dataset that consists of hourly time series data on temperature, humidity, air pressure, wind speed, and wind direction in New York (NY), San Francisco (SF). and Houston (HS). Given the last 24 hours of time series data, the task is to predict whether it will rain in the next 24 hours.
Multimodal Time Series Benchmark (MTBench) (Chen et al. 2025b) is a cross-domain dataset, covering two domains: weather and finance. It comprises paired time-series and textual data, in- cluding financial news with corresponding stock price movements and weather reports aligned with historical temperature records. The richness of MTBench enables the formulation of diverse tasks that require deep understanding of both text and time-series data, including time-series forecasting, semantic and technical trend analysis, and news-driven QA.
https:://pwosc.orr//veeeett /html/
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FinMulliiime (Xu et al. 2025b) is dataset in the field of Economy, which aligns four distinct modalities: financial news, structured financial tables, K-line technical charts, and stock price time series across both the S&PP 500 and HS 300 universes. Covering 5,105 stocks from 2009 to 2025 in the United States and China. This dataset provides minute-level, daily, and quarterly resolutions, thus capturing short, medium, and long-term market signals with high fidelity.
MDD (Mumtaz, 2016) is a dataset that includes 33 patients diagnosed with major depressive dis- order (MDD) according to DSM-IV criteria without psychotic symptoms (18 females, mean age 40.33, SD = 12.861) and 30 age-matched healthy controls (9 females, mean age 38.23, SD = 15.64). Clinical assessments comprised the Beck Depression Inventory-II (BDI-II) and the Hospi- tal Auxiety and Depression Scale (HADS), while neurophysiological data consisted of resting-stale EEG recordings. EEG was collected with a 19-channel cap based on the 10-20 system, using linked- ear (LE) reference initially and later re-referenced to infinity reference (IR).
TUEV (The TUH EEG Events Corpus) (Obeid & Picone| 2016) is a subset of TUEG, which focuses clinical electroencephalogrann (EEG) recordings conducted at Temple University Hospi- tal (TUH) from 2002 to 2013 (and beyond). TUEV contains annotations of EEG segments as one of six classes: (1) spike and sharp wave (SPSW), (2) generalized periodic epileptiform discharges (GPED), (3) periodic lateralized epileptiform discharges (PLED), (4) eye movement (EYEM), (5)
artifact (ARTF) and (6) background (BCKG).
WBCIC SHU (Yang et al. 2025b) dataset is a comprehensive motor imagery brain computer inter- face dataset comprising data from 62 subjects across three recording sessions. This dataset includes two paradigms: the first involves upper limb movements with left and right hand-grasping with 51
subjects participating; the second adds foot-hooking task, involving 11 subjects.
SDeepEDF (Kemp et al, 2000) is a comprehensive dataset containing 153 overnight polysomnog- raphy (PSG) recordings from 78 subjects. Each recording includes signals of two-channel EEG (Fpz-Cz and Pz-Cz) and one EOG, sampled at 100Hz. We process the data by utilizing only the Fpz-Cz EEG channel, excluding the "MOVEMENT' and 'UNKNOWN' stages, and combining the N3 and N4 stages into one sleep stage N3, to conform to the latest AASM standard, yielding the five target sleep stages: W, N1, N2, N3, and REM. The EEG signals are processed with a 0.5-45Hz bandpass Butterworth filter to remove background noise.
The textETT (Ge al. 2025) dataset is an augmented version of the ETT dataset, where each time series fragment is paired with a textual description. In this dataset, the input is a natural language caption, and the output is the corresponding time series segment. Following the settings introduced in the original paper, we use ETThl and construct fragment-level datasets with lengths of 24, 48 and 96.
ETT (Wu et al. )021) is dataset that contains measurements from electricity transformers, includ- ing six load features and oil temperature. We use the ETThl subset, which is hourly recorded from July 2016 to June 2018. Following the standard protocol, we split the dataset chronologically into training, validation, and test sets with a ratio of 6:2:2. In Sci-TS, we focus on the OT (oil tempera- ture) column for univariate forecasting with context length of 96 and prediction lengths of 96 and 720, allowing us to assess model performance on both short-term and long-term predictions.
(Wang et al. 2024b) is a multi-domain dataset that combines time series data from multiple human-driven domains with aligned news and contextual information. The time series covers four domains: Traffic (hourly traffic volume), Exchange (daily exchange rates), Bitcoin (daily Bitcoin price), and Electricity (half-hourly electricity demand from the Australian Energy Market Operator, AEMO). To enrich the forecasting task with external signals. the dataset incorporates related news. Part of the news content is drawn from the GDELT database, which tracks global events in over 100 languages, while additional domain -omain-specific news is collected from sources such as News Corp Australia and Yahoo Finance.
NewsForecast
PTB XL (Wagner et al. 2020) dataset consists of 21,837 clinical 12-lead ECG recordings (10- second each) from 18,885 patients, sampled originally at 500 Hz (also provided downsampled to 100 Hz). The records are multi-label annotated with up to 71 SCP-ECG diagnosticfform/rtyttn statements, with hierarchy into super- and subclass diagnostic labels, and include metadata like age, sex, signal quality, recording device and site.
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MetroTraffic (Metro Interstate Traffic Volume) (Wang al. 2025a) is dataset that contains hourly westbound traffic volumes on Interstate 94 between Minneapolis and St. Paul, MN, from 2012to 2018, including 63 holidays. In Sci TS, we set two forecast modes: forecasting the next 3 days from the past 14 days and forecasting the next 7 days from the past 21 days. By sliding on the raw sequence data according to different prediction lengths, finally obtained approximately 25k pieces of data.
CWRU (Case Western Reserve University) (Center) bearing dataset contains vibration data from normal and artificially faulted ball bearings, collected using a HP Reliance Electric motor. Faults (created by dectro-discharge machining) include defects on the inner raceway. the rolling element (ball), and the outer raceway. with fault diameters of 0.007", 0.014", 0.021", and 0.028". Vibration measured at multiple sensor locations: drive end (DE), fan end (FF), and base (BA), under motor loads from 0 to 3 HP and motor speeds around 1 1720-1797 RPM.
MIMI DUE (Tanabe et al., (202T) is dataset that consists of the normal and abnormal operating sounds of five different types of industrial machines. The data for each machine type includes six (ubsets(three utilized in Sci-TS) called "sections", and each section roughly corresponds to a single product. Each section consists of data from two domains, i.e., the source domain and the target domain, with different conditions such as operating speed and environmental noise.
RadSeg (Radar Segmentation Dataset) (Huang al. 2024 is synthetic radar dataset designed for building semantic segmentation models for radar activity recognition. Unlike existing radio classification datasets that only provide signal-wise annotations for short and isolated IQ sequences, RadSeg provides sample-wise annotations for interleaved radar pulse activities that extend across a long time horizon. RadSeg contains pulsed radar signals varying signal-to-noise ratios (SNRs) between -20 and 20 dB with a resolution of 0.5 dB.
RadarComm (Jagannath & Jagannath| 2021) is wireless signal dataset. The lack of existing multitask labelled datasets for machine learning for wireless communication is the prime motivation urging this release. RadarCommDatasee is the first of its kind, multitask labelled dataset released to help the research community advance machine learning for wireless communication. The dataset contains radar and communication waveforms.
Chaotic (Gilpin 2023) datasets is a collection of multivariate time series generated from 135 well- known chaotic systems, each numerically integrated produce sequences of length 10,000 with highly coupled dynamics. To account for integration precision, the dataset is divided into three categories: coarse, medium, and fine. We select the coarse subset and, after filtering out anomalous cases, retain 116 systems. Each task uses a context length of 100 to forecast the next 20 time steps.
E DESCRIPTION AND CONFIGURATION OF THE ASSESSED MODELS
This section provides descriptions and configurations of assessed models, including text-only LLMs, multimodal LLMs, and unified time-series models. Our preliminary experiments suggest that rea- soning provides little benefit for time-series tasks in SciTS for the assessed models. Given resource constraint, we set the reasoni fort
fort parameter to mi n nimal 1 for all evaluations.
E.1 TEXT LLMS
1. GPT-4.1-ming A lightweight version of OpenAl's GPT-4 series, optimized for efficiency while maintaining strong reasoning and language understanding capabilities, with context length of IM tokens.
2. Gemini-2.5-Flastf7 The most advanced model from Google DeepMind's Gemini family, designed for fast inference with competitive performance across diverse language tasks, with context length of IM tokens.
3. DeepSeek-V3 (Liu et al. 2024a
An advanced open-source LLM developed by
DeepSeek, with a context length of 128K tokens.
2- flash
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4. Llama3-8B (Grattafiori et al.
An open-source LLM from Meta's Llama series, widely adopted for research applications, with a context length of 8k tokens.
2024
5. Qwen3-4B (Yang et al. 2025a In A smaller version of Qwen3, providing lightweight inference while maintaining competitive performance, with a suggested context length of 128k tokens.
6. Qwen3-8B (Yang et al.
An open source LLM from Alibaba's Qwen series, with a suggested context length of 128k tokens.
2025a
E.2 MULTIMODAL LLMS
1. GPT-5-min 12 A multimodal variant of OpenAl's most advanced GPT-5 family, with
context length 400K tokens.
2. Gemini-2.5-Flass We also use for evaluation with multimodal inputs since native multimodal LLM.
3. InternVL3..54B WWag
2025c
smaller 4B- 1arpamaeeter version of AnterVLL...
4. Inte5nVL3.5-8B (Wang et al.
2025c 15
An advanced open-source multimodal LLM from
Shanghai AI Lab, with context length of 32K tokens.
5. Qwen2.5-VI.-7B (Bai et al.
2025b 16
multimodal model from Alibaba's Qwen2.5 se-
ries, with context length of 128K tokens.
E.3 TIME SERIES MODELS
For time series models, we only incorporate models that support zero-shot inference. Therefore, foundation feature extraction models like PatchTST (Nie et al.] 2023) are excluded. Among the baseline models listed below, Moirai, Time-MOE, and Chronos are limited to forecasting tasks. In contrast, ChatTS focuses solely understanding tasks, while UniTS is unified model capable of handling both understanding and generation tasks, including forecasting and imputation.
1. Moirai (Woo et al. 2024). A universal time series forecasting model builds a masked encoder Transformer. It incorporates multi-patch projections to handle various data and supports multi-channel forecasting by flattening into single sequence and using its spe- cialized attention mechanism. We use Moirai-large (311M parameters), Moirai-base (91M parameters), Moirai-small (14M parameters) for evaluation. It supports multi-variate to univariate and univariate forecasting. We use univariate forecasting for multi-variate to multi-variate case.
2. Time-MoF.
(Shi et al. 2025).The The first time series foundation model that incorporates ofxture-of-Expert (MoE) architecture, scaling up to 2.4 billion parameters and trained from scratch. It employs point-wise tokenisation and leverages multi-csollution scheduling to produce forecasts at multiple scales simultancously. The model is currently only capable doing univariate time series forecasting. We use Time-MoE- Time-MoE-lane (200M parameters) for evaluation. It only supports univariate forecasting.
(50M parameters), We use univariate forecasting for multi-variate to multi-variate case.
3. Chronos Ansari et al. 2024). A family of pretrained time series forecasting models built on language model architectures. It transforms time series into discrete token sequences through scaling and quantization, and is trained using cross-entropy loss. During infer- ence, Chronos generates probabilistic forecasts by sampling multiple future trajectories
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from the historical context. We Chronos-bolt-ming (21M parameters) and Chronos- bolt-base (205M parameters) for evaluation. only supports univariate forecasting. We univairate forecasting for multi-variate to multi multi-variate case.
4. ChatTS (Xie et al. 2025) A universal time-series dialogue and understanding model con- structed upon Large Language Models (LLMs) and equipped with time-series encoder. is capable of processing multivariate sequences (with maximum support for 50-channel data input) and variable-length sequences, while supporting tasks such as trend analysis, clustering, and reasoning- based question answering. But it exclusively generates text out- put and does not support time-series generation tasks.
5. UnITS (Gao et al. 2024). unified time series model that can handle a wide range of tasks within single architecture It supports forecasting, anomaly detection, classifica- tion, and imputation, providing a general solution across diverse applications. supports multivariate signal but requires input dimension to be the same as output dimension.
F FULL RESULTS ON SCITS
This section provides detailed results for each tasks. We define the following abbreviations for com- mon failure reasons: (i) too long input/output sequence (TLS) where input length exceeds model's context length output prediction length exceeds model's
predict length; (ii) too many channels (TMC) where model doesn't support the number of input/output channels; (iii) instruction followed (INF) where model does not generate output of required length or format. Under- standing tasks are evaluated by accuracy and Fl. Generation Tasks are evaluated by MAE, MAPE, success rate (SR). Not supported tasks are marked as *_" All results presented percentage.
Table Detailed results of SciTS (Astronomy and Earth Science).
Domain
Astronomy
Science
Task ID
ASU01
ASG02
ASU03
EAU01
EAG02
Metric
Acc
F1
MAPE
SR
Acc
FI
Acc
F1
MAPE
SR
Large
Language Models (Text input)
GPT-4.1-mini
51.3
67.2
97.5
96.6
32.3
15.6
51.4
67.0
64.9
99.9
Semini2.5-Flast
50.6
64.1
98.3
84.3
46.3
16.3
51.8
67.6
62.9
99.8
DeepSeck-Vi V3
48.0
1.1
99.4
17.0
32.9
12.3
53.8
40,2
86.6
96.1
Llama3-8B
TLS
TLS
TLS
TLS
8.3
5.1
TLS
TLS
TLS
TLS
Qwen3-8B
TLS
TLS
TLS
TLS
14.7
6.2
45.7
47.2
28.1
0.1
Qwen3-4B
TLS
TLS
TLS
TLS
47.2
15.9
47.2
15.9
76.5
0.1
Multimodal Large Language Models (Text+Image input)
GPT-5
50.4
65.7
50.3
90.8
50.5
18.9
51.6
67.6
25.4
100.0
Gemini2.5-PF 5-Flssh
53.5
77.6
71.4
45.1
15.2
61.7
72.5
24.6
99.9
InternVL3.55SB
55.7
41.7
50.8
30.3
2.9
1.0
76.4
81.0
2.2e2
99.9
Intern LB3
53.9
80.5
100.0
39.7
10.7
63.8
73.6
2.8e2
99.8
Qwen2.5-VL-7B
48.4
2.9
6.7
1.5
27.5
11.3
75.8
80.6
53.5
99.5
Time Series Models
Moirai-lange-3IIM
Moirai-bacc-9IM
Moirai-small-14M
TimeMoF-hase-50M
TimeMoFFlarge-210M
Maronos-bolt-mini222
Strnno-bolt-baae-205M
ChaTS
TLS
TL.S
TLS
TLS
38.3
11.3
61.7
64.8
3.3
60.1
UniTS
52.2
68.6
3.3e6
100.0
11.5
7.8
49.6
0
INF
INF
TimeOmni
69.0
73.5
2.8
100.0
87.8
72.8
80.9
82.5
2.2
100.0
x32
<32_pretrrin_chheckpp
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Table 9: Detailed results of SciTS (Bioacoustics and Meteorology).
Domain
Bioacoustics
Metcorology
Task ID
BIUO1
BIU02
BIU03
MEU01
MHU02
Metric
Acc
Ace
FI
Ace
FI
Ace
FI
Large Language Models (Text input)
GPT-4.1-mini
1.0
0.2
13.3
7.3
17.9
12.7
53.7
44.0
47.0
36.0
Gemini2.5-Flash
4.7
1.5
44.3
16.9
15.6
12.4
50.6
60.9
35.2
41.2
DeepSeek-V3 V3
0.1
0.0
2.3
0.9
16.9
5.8
49.0
59.3
52.9
34.8
Llama3-8B
TLS
TLS
TLS
TLS
TLS
TLS
48.7
65.3
27.8
43.2
Qwen3-8B BBBBBBBB
TLS
TLS
TLS
TLS
TLS
TLS
53.5
64.9
40.1
41.4
Qwen3-4B
TLS
TLS
TLS
TLS
TLS
TLS
51.3
67.1
73.4
0.0
Multimodal Large Language Models (Text+Image input)
GPT-5
2.8
0.8
33.4
13.4
26.4
17.9
54.4
30.4
30.6
42.9
Gemini2,5-Fii
2.2
0.9
28.4
14.0
16.8
8.3
49.5
64.1
33.0
41.7
Intern VL3.5nVL33.588F
0.1
3.2
16.8
10.8
48.9
65.4
51.7
33.4
Intern VM3,5VLL3.54P
2.0
0.1
6.1
2.4
17.8
8.8
54.3
63.1
73.3
0.0
Qwen2.5 VL-7B
3.3
0.3
81.0
22.9
16.7
4.8
54.1
36.4
0.0
0.0
Time Series Models
Moirai-large-31I
Moirai-base-91M
Moirai-small-14M
TimeMoo--usse-500
TimeMMoE-lare-22000
Chronos-bee Moonos-bolt-miii-2MMM
Chronos-boll bottoos-oot-bbs--200 base-205M
ChaTS
TLS
TLS
TLS
TLS
TLS
TLS
56.3
52.8
36.5
41.1
UniTS
6.1
0.6
82.1
18.9
16.7
4.8
48.7
0
73.4
0
TimeOmni
42.6
34.1
83.4
51.2
89.6
89.2
52.0
65.2
48.4
38.8
Table 10: Detailed results SciTS (Meteorology and Economics).
Domain
Meteorology
Economics
Task ID
MEG03
MEU04
BCG01
ECG02
Metric
MAE
MAPR
Acc
MAR
MAPE
MAE
MAPE
SR
Large Language Models (Text input)
GPT-411 1-mini
3.9
42.1
49.6
55.9
22.3
193.1
89.8
3.5
3.7
89.8
Gemini2.5-Flam
4.3
62.2
57.9
53.3
0,7
2.3
65.2
3.0
3.1
65.2
DeepSeek- V3
4.7
46.4
30,9
55.5
1.1
3.2
90.1
4.9
5.3
90.1
Llama3-8B
INF
INF
39.0
INF
INF
INF
INF
Qwen3-8B
27.5
56.7
INF
INF
1.0
1.3
Qwen3-4B
INF
INF
INF
55.7
INF
INF
INF
0.8
Multimodal Large Language Models (Text+Image input)
GPT-5
3.6
37.6
51.8
62.5
INF
INF
0.0
3.1
3.8
53.5
5emani2.5-Flash
5.6
53.1
37.2
51.1
0.7
3.1
40.2
3.4
3.6
26.1
Intem
3.8
37.0
26.9
63.2
INF
INF
4.7
4.8
17.5
VL..5-
4.4
78.5
46.0
INF
INF
INF
INF
INF
Qwcn2.5-VL-7B
5.6
31.1
0.2
47.9
INF
INF
INF
3.1
0.3
Time Series Models
Moirai-large-3
31
10.61
51.7
100.0
0.5
1.4
100.0
2.0
2.1
100.0
Moirai-hasc-91MM
10.5
59.8
100.0
0.5
1.4
100.0
2.0
2.1
100.0
Moirai-small-Iae
77.4
32.9
100.0
1.5
100.0
2.1
100.0
TimeMoF.base-5000
2.9
38.5
100.0
2.5
100.0
TimeMoE-largee 2000
3.0
39.0
100.0
2.6
2.7
100.0
Chronos-bole Chronos-bolt-mini-
bolt-mmni--2MM
3.1
42.1
100.0
2.1
2.3
100.0
Chronos-bott-base-DDee
3.1
41.5
100.0
2.1
2.3
100.0
ChaTS
59.6
UniTS
3.5
42.0
100.0
29.5
TMC
TMC 6.2
TMC
3.6
4.0
96.9
TimeOmni
3.0
37.5
100.0
80.0
2.1
100.0
4.3
4.5
100.0
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Table 11: Detailed results of SciTS (Economics and Neuroscience).
Domain
Economics Acc
Neuroscience
NEUO1
NEU02
NEG03 MAPE
NEG04
Metric
MAE
MAE
MAPE
Large Language Models (Text input)
GPT-4.1-mine
90.4
50.0
0.0
23.3
16.1
49.0
95.2
96.4
2.7
22.7
94.4
Gemini2.5-Flaste
87.8
50.0
0.0
9.5
5.8
8.4
63.5
99.2
1.9
13.2
99.2
DeepScek-V3 V3
91.1
50.0
0.0
37.3
13.6
2.1
4.3
3.1
2.1
13.6
98.7
Llama3-8B
TLS
11.7
151.5
3.1
4.6
102.7
28.7
Qwen3-8B
83.5
TLS
TLS
11.2
9.3
105.8
26.4
5.2
63.8
Qwen3-4B
77.9
TLS
TLS
26.2
9.8
11.6
100.8
43.6
6.2
59.4
77.5
Multimodal Large Language Models (Text=Image input)
GPT-5
83,8
50.0
1.3
15.1
13.3
10.3
74.3
97.2
19.3
50.6
2.4
5emini2,5-Flash
82.9
44.9
53.7
13.1
11.6
INF
INF
INF
110.7
97.4
21.2
VL3
64.7
90.7
73.6
15.2
215.8
LL355
86.1
54.6
20.4
21.
7.9
16.7
189.6
43.9
328.1
340.4
28.7
Qwen2.5-VL-7B
81.9
50.0
0.0
0.5
2.4
20.1
204.8
18.1
53.5
749.2
1.9
Time Series Models
Moirai-large-33
311M
8.1
59.1
100,0
Moimi--ase-911
7.9
56.5
100.0
Moirai-small-IP
7.9
56.5
100.0
TimeMoE-buse-5000
5.9
66.9
100.0
JimeMoE-large-2000
5.8
70.1
100.0
Chronos-bolt-mimile
8.3
75.1
100.0
moono--oollbuus-20550
78.5
ChaTS
79.2
21.8
14.7
24.5
UniTS
27.1
73.4
63.8
22.7
13.0
10.7
95.2
46.4
66.4
89.3
100.0
TimeOmni
96.4
67.2
76.6
43.0
35.7
10.0
78.7
100.0
2.1
14.5
100.0
Table 12: Detailed results of SciTS (Neuroscience and Energy).
Domain
Neuroscience
Energy
Task ID
NEUOS Acc
NEU06 Acc
ENG0I MAPE
ENG02
Metric
MAE
MAE
MAPE
SR
Large Language Models (Text input)
1-mini
32.6
25.7
17.1
12.0
19.6
218.4
41.9
4.5
125.0
1.4
Gemini2.5-Flam
30.4
30.9
19.3
14.0
8.2
101.7
56.5
2.5
72.5
5.9
DeepSeek-V V3
33.5
17.3
7.1
7.5
1.3
16.4
52.0
4.1
117.2
46.1
L.lama38BB
T1.S
T1S
TLS
T1.
1.2
15.0
16.6
2.1
62.4
12.7
Qwen3-8B
TLS
1.0
13.1
33.3
70.2
18.2
Qwen3-4B
1.2
71.2
34.5
Multimodal T Large Language Models (Text+Tmage input)
GPT-5
32.0
25.1
26.2
16.1
INF
INF
INF
1.8
56.1
4.5
Gemini2,55Flash
33.6
27.5
28.5
26.5
INF
INF
INF
3.3
103.9
7.4
Intem
33.2
17.0
6.8
4.9
INF
53.0
0.4
IntemVLL3.544B
33.9
22.1
34.0
11.2
INF
79.2
0.02
Qwen2.5-VL-7B
16.7
13.7
INF
INF
INF
INF
Time Series Models
Moirai-large-3111
3.8
121.2
100.0
Moirai-base-91MM
3.7
116.7
100.0
Moirai-small-
76.6
100.0
Tlim:MoE-buse-S0k,
71.6
100.0
Mimeeee--arge-- 200M
70.4
100.0
MM
2.5
72.6
100.0
2.5
73.7
100.0
ChaTS
TMC
TMC
10.8
21.9
UniTS
33.3
16.7
14.2
10,3
2.4
70.1
100.0
TimeOmni
50.4
60.4
73.0
67.8
1.4
19.3
100.0
2.2
68.6
100.0
30

---

Published as a conference paper at ICLR 2026
Table 13: Detailed results of SciTS (Energy and Physiology).
Domain
Energy
Physiology
Task ID
ENG03 MAPR
ENG04 MAPE
ENGOS MAPE
PHUO1
Metric
MAE
SR
MAR
SR
MAE
Large Language Models (Texi imput)
GPT-4.1-ma 1-mini
334.0
8.3
96.0
54.1
104.7
100.0
2.5
28.2
100.0
24.0
Gemini2.5-Flash
451.2
9.6
99.0
50.8
96.5
98.5
25.9
100.0
20.7
DeepScck-V3
294.0
98.0
90.2
61.4
66.7
27.8
100.0
28.9
Llama3-8B
219.5
8.5
16.0
0.2
32.8
19.7
29.4
82.0
41.5
TLS
Qwen3-8B
301.7
7.8
96.0
0.2
78.6
37.9
9.8
70.9
70.8
TLS
Qwen3-4B
291.9
7.5
99.0
16.1
90.5
42.4
24.7
54.6
72.3
TL.S
Multimodal Large Language Models (Text+Image inpul)
GPT-5
471.6
11.2
76.0
4.0
122.6
90.9
0.3
119.0
81.5
23.1
Gemini2.5-Flash
621.5
15.6
53.0
72.0
62.9
10.6
272.5
79.8
15.4
23.5
IntemmL13.5-8B
429.6
42.5
1.0
43.6
60.1
93.9
600.8
6.5c3
56.9
21.1
2.6e3
49.6
5.0
88.8
327.0
39.4
255.2
17.4
Qwen2.5-VI.-7B
INF
INF
0.0
0.4
$14.7
9.1
INF
INF
INF
0.2
Time Series Models
Moirai-large-31I 31
503.3
12.8
100.0
20.2
43.8
100.0
Moirai-buse-911
433.9
11.1
100.0
20.3
44,4
100.0
Moirai-small-I1 -4MM
493.7
12.4
100.0
16.7
51.6
100.0
TimeMoE-base-5000
469.0
11.3
100.0
10.4
51.1
100.0
TimeMoE-large-2000
491.1
11.6
100.0
8.1
55.5
100.0
Chronos-bolk how---oooo---i-i-- miil--mnni2111 I
514.7
12.4
100.0
10.4
38.1
100.0
516.2
12.0
100.0
13.2
38.7
100.0
ChaTS
27.5
UniTS
533.1
12.8
100.0
51.8
115.4
66.7
48.6
92.5
100.0
8.3
TimeOmni
300.8
7.4
100.0
60.0
111.9
100.0
44.1
100.0
44.5
Table 14: Detailed results of SciTS (Physiology).
Domain Task ID Metric
Physiology
PHG02 MAPE
PHG03 MAPE
PHUM4 FI
PHUOS
MAE
SR
MAE
SR
Ace
Acc
F1
Large Language Models (Text input)
GPT-4.1-mini
18.3
1.1e3
94.2
20.6
$7.0
52.7
Gemini2.5-Flash
10.1
110.8
99.0
0.5
8.9
98.7
53.2
64.8
42.0
19.5
DeepSeek-Ve V3
18.8
200.1
92.2
0.8
99.6
63.3
50.7
86.0
Llama3-SB
2.4
289.4
15.8
11.5
101.6
32.3
49.6
66.3
11.5
18.9
Qwen3-8B
8.6
182.7
37.6
7.9
59.2
78.6
50.0
66.7
11.0
18.8
Qwen3-4B
17.6
223.1
44.0
5.6
66.0
81.8
50.8
66.9
10.4
18.8
Multimodal Large Language Models (Text+Image input)
GPT-5
16.6
155.3
97.4
32.2
182,9
60.5
45.5
47.8
56.5
16.9
Gemini2.5-Flass
25.0
185.2
36.9
296.1
2.3e3
25.1
42.9
59.0
16.7
18.3
Intern\1.3.5-88
12.2
227.1
72.8
26.
569.7
50.9
50.0
66.7
10.4
18.8
VL355mVV3..5-4B
15.2
855.
35.7
125.8
3.
51.8
49.9
66.6
10.5
18.8
Qwcn2.5-VL-7B
15.5
384.3
21.5
54.7
950.8
10.1
43.7
49.6
87.4
Time Series Models
Moirai-large-31 IM
11.6
116.9
100.0
Moirai-base-9IM
11.9
116.5
100.0
Moirai-small-I4Ng
12.9
117.5
100.0
TimeMoE-buse-S00
7.4
78.7
100.0
TimeMoE-large
7.3
80.2
100.0
Chronos- coomos-bolt-nane
bolt-mini-211
11.2
114.8
100.0
10.8
109.3
100.0
ChaTS
51.3
59.5
10.4
18.7
UniTS
14.3
135.9
44.1
16.7
124.4
100.0
89.6
47.3
10.4
18.8
TimeOmni
15.4
163.0
100.0
2.1
20.4
100.0
90.5
92.7
38.8
23.0
31

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Published as a conference paper at ICLR 2026
Table 15: Detailed results of SciT'S (Physiology and Urbanism).
Domain
Physiology
Urbanism
Task ID
PHU06
URGOL MAPE
URG02 MAPE
URG03
Metric
Acc
F1
MAE
SR
MAE
SR
MAE
MAPE
SR
Large
Language Models (Text input)
I-T--.1-mini
41.7
27.6
0.03
320.6
18.6
447.9
705.4
90.3
119.0
47.6
98.5
Gemini2.5-Flase
27.3
22.1
0.04
246.0
23.3
378.8
290.4
77.4
93.9
33.3
100.0
DeepSeck-V. V3
46.3
22.1
0.04
350.0
18.6
686.3
935.6
50.0
111.6
37.3
100.0
Llama3-8B
37.8
12.5
0.03
282.7
7.0
1.6c3
2.9c3
4.8
277.2
300.3
24.6
Qwen3-8B
16.1
14.9
0.04
262.0
53.5
840.4
2.3c3
12.9
259.2
$15.7
78.5
Qwen3-4B
9.0
4.3
0.03
158.7
69.8
571.9
1.1e3
22.6
289.4
360.2
76.9
Multimodal Large Language Models <Text+Image imput)
GPT-S
36.8
14.1
0.04
182.1
58.1
432.5
704.8
77.4
610.2
504.5
55.4
Gemini2.5-Flash
31.2
9.3
0.04
351.9
16.3
657.4
1.0c3
40.3
1.4c3
1.4c3
40.0
IntermV1.3.588
39.8
13.3
0.07
307.4
30.2
751.2
1.3e3
82.3
772.7
575.7
23.1
ILternVL3.554p
40.8
9.9
0.05
253.7
2.3
INF
INF
INF
1.4e3
1.0c3
32.3
Qwen2.5.
41.3
14.4
0.06
686.4
4.7
INF
INF
137.4
Time Series Models
Moirai-large-31
0.04
294.7
100.0
162.8
189.7
100.0
Moirai-base-91MM
0.04
100.0
160.7
195.5
100.0
Moirai-small-14N
0.04
374.9
100.0
199.8
287.9
100.0
TimeMoE-base-500
0.03
204.4
100.0
72.5
55.4
100.0
AmmeMoF-large-2000
0.03
218.4
100.0
67.2
53.8
100.0
Chronos- bolt moolt-mini-21111 2
0.03
143.9
100.0
72.2
58.8
100.0
Sironos-ool-base---e
0.03
139.3
100.0
63.5
48.5
100.0
ChaTS
17.7
UniTS
5.5
17.4
0.04
389.7
100.0
442.5
1.3c3
69.4
451.1
628.4
100.0
TimcOmni
26.8
23.4
0.03
247.0
100.0
427.4
1.1c3
100.0
143.0
132.5
100.0
Table 16: Detailed results of SciTS (Urbanism and Manufacturing).
Domain
Urbanism
Manufacturing
Task ID
URU04
URG05 MAPE
MFU01
MFU02
MFU03
Metric
Acc
FI
MAE
SR
Acc
F1
Ace
FI
Acc
FI
Large Language Models (Text input)
GPT-4.1-mint
65.6
64.4
1.2e3
126.6
100.0
4.5
3.2
29.3
34.9
51.2
56.9
Gemini2.5-Flas 5-Fassh
53.3
64.6
1.2e3
98.6
100.0
20.5
15.1
27.0
4.6
50.4
66.8
DeepSeck-V3
50.0
64.7
1.8c3
296.7
93.0
0.0
0.0
29.5
0.0
50.1
62.5
Llama3-8B
50.8
67.4
1.9e3
297.3
93.4
TLSS
T1S
TLS
TLS
T1SS
TLSS
Qwen3-8B
58.2
66.2
1.5e3
159.4
89.2
T1SS
T1S
TLS
TLS
T1S
TLSS
Qwen3-4B
47.5
62.8
2.0c3
256.3
93.2
TLS
TLS
TLS
TLS
TLS
TLS
Multimodal Large Language Models (Text+Image input)
GPT-5
59.0
64.8
1.4e3
71.1
72.9
22.8
32.5
32.0
45.5
49.6
37.1
Gemini2.5-Flust
50.8
67.0
1.1e3
114.6
91.2
30.3
32.1
32.5
32.1
50.5
54.0
IntemVL33.553
50.8
67.4
1.5e3
173.9
99.8
10.0
0.0
15.3
21.6
49.8
37.2
VL3.5-L
50.8
67.4
1.7c3
158.1
65.2
INF
INF
INF
INF
49.6
11.7
Qwen2.5-VLL-7B
63.9
58.5
836.5
116.3
16.9
10.3
1.6
10.5
0.0
48.4
17.7
Time Series Models
Moirai-large-31 31
968.8
74.6
100.0
Moirai-bat Sitri-bbse-9111 9
811.6
52.2
100.0
Moirai-small- small-14M
794.1
50.8
100.0
691.5
89.5
100.0
TimeMoE-larged 100Me-200M
687.1
84.4
100.0
coomoos-oott-mii----
759.5
61.1
100.0
Stronos-bolt-base2200
800.6
70.6
100.0
ChaTS
48.7
65.4
0.0
0.0
0.0
0.0
TLS
TLS
UniTS
50.8
67.4
TLS
TLS
TLS
28.0
33.0
41.5
45.8
50.0
66.7
TimeOmni
47.5
64.8
1.7c3
174.0
100.0
90.0
90.1
95.8
94.8
47.8
61.0
32

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Published as a conference paper at ICLR 2026
Table 17: Detailed results of SciTS (Radar and Math).
Domain
Radar
Math
Task ID
RAUOI
RAU02
MAG01 MAPR
Metrie
Acc
FI
Acc
FI
MAE
SR
Large Language Models (Text input)
GPT-4.I-mini
24.4
24.6
13.4
10.6
8.0
1.1e3
96.2
Gemini2.5-Flash
21.3
20.9
15.0
13.5
6.7
442.0
92.6
DecpSeck-V3
27.6
19.4
11.3
4.2
5.4
566.0
95.2
Llama3-88
16.0
10.8
13.1
7.5
INF
INF
0.0
Qwen3-8B
18.4
10.2
12.3
2.8
INF
INF
0.0
Qwen3-4B
13.6
8.4
13.1
8.2
INF
INF
0.0
Multimodal Large Language Models (Text+Image input)
GPT-5
26.9
24.3
10.7
9.1
4.4
1.4c3
97.9
Gemini2.5-Flash
30.8
31.6
12.5
11.3
5.2
510.9
98.1
IntermVL3,5-8B
1.6
1.7
7.0
4.6
INF
INF
0.0
Intern VB3
19.7
6.9
12.5
INF
0.0
Qwen2.5-VL-7B
13.4
INF
0.0
Time Series Models
Moirai-large-311n
3.2
360.1
100.0
Moirai-hase-91N
3.0
359.9
100.0
Moirai-small-14M
311.6
100.0
TimeMMoE-ass--500
573.0
100.0
TimeeMoE-arge-2000
2.4
1.2e3
100.0
Shronos-holt-mini-222
3.0
394.4
100.0
Mhronus-boll-basee
3.0
440.4
100.0
ChaTS
31.1
23.4
11.2
4.4
UniTS
18.5
18.4
12.5
TT.S
TT.S
TT.S
TimeOmni
82.9
51.9
54.9
3.7
656.5
100.0
33

---

Published as a conference paper at ICLR 2026
G ABLATION STUDY ON TIMEOMNI
We conducted ablations on the reprogramming module and patch-expert routing mechanism of TimeOmni to quantify the contribution of each component. Results shown Table 18 and Table
Ablation the reprogramming module. We replace the reprogramming module with layer MLP using GELU activations and Dropout. This simplification leads to consistent perfor- mance degradation across most tasks. The results indicate that the reprogramming module provides stronger alignment between time-series inputs and the text LLM,, improving robustness and gener- alization across diverse tasks.
Ablation on the patch-expert routing module. We replace the patch-expert routing mechanism with fixed patch size of 1024 while keeping all other settings unchanged. Because SeiTS contains very long signals, using patch size smaller than 1024 would cause GPU out-of-memory, making 1024 the smallest feasible choice. This fixed patch size has little impact on medium-length tasks (e.g.. EAU01). and can fective training data for that patch. However, performance degrades on very long sequences (c.g., BIU03), where the model fails to capture long-range dependencies. Additionally, SeiTS includes short sequences (e.g., ASU03) whose lengths are far below 1024. For these tasks, forcing a 1024- length patch collapses the entire sequence into a single patch, introducing structural inefficiencies that further hurt performance. Overall, this ablation demonstrates that patch-expert routing is es- sential for handling the wide range of temporal scales in scientific time series, from very short extremely long.
slightly improve them, since using single expert increases the ef-
Table 18: Ablation study on TimeOmni.
Domain
Task ID
Metric
TimeOmni
w/o Reprogramming
w/o Rounter
Acc
69.0
62.3
58.4
ASU01
F1
73.5
70.6
50.4
Astronomy
ASG02
MAPE
2.8
10.2
14.3
Acc
87.8
86.8
56.5
ASU03
F1
72.8
77.0
25.0
Acc
80.9
77.4
81.5
EAU01
Earth Science
F1
82.5
81.5
84.1
EAG02
MAPE
2.2
2.2
10.7
42.6
40.1
BIU01
34.1
32.2
31.8
Acc
83.4
84.2
83.8
Bioacoustics
BIU02
F1
51.2
44.7
46.7
Acc
89.6
80.5
73.5
ВIU03
F1
89.2
80.7
79.4
Acc
52.0
42.0
50.4
MEU01
F1
65.2
62.6
63.1
Acc
48.4
50.8
48.9
MBU02
Meteorology
F1
38.8
44.4
40.6
MAE
3.0
3.0
3.1
MEG03
MAPE
37.5
37.7
38.3
MEU04
Acc
80.0
93.3
MAE
2.1
2.1
2.2
ECG01
MAPE
6.2
6.1
6.1
Economics
MAE
4.3
4.3
4.3
ECG02
MAPE
4.5
4.5
4.5
ECU03
Acc
96.4
8.6
93.1
34

---

Published as a conference paper at ICLR 2026
Table 19: Ablation study on TimeOmni (Cont.).
Domain
Task II
Metrie
TimeOmni
w/o Reprogramming
w/o Rounter
Acc
67.2
52.3
64.0
NEU01
Fl
76.6
80.7
77.7
50.0
49.4
NEU02
FI
35.7
32.8
43.6
MAE
10.0
9.8
9.6
NEG03
MAPE
78.7
85.4
80.9
Neuroscience
MAE
2.1
2.4
7,4
NEG04
MAPE
14.5
16.2
90.6
Acc
50.4
50.7
49,6
NEU05
FI
60.4
60.8
59.4
Acc
73.0
69.5
39.6
NEU06
F1
67.8
58.7
47.3
MAE
8.2
8.2
8.2
ENG01
MAPE
100.0
100.0
100.0
MAE
2.2
2.2
2.1
ENG02
MAPE
68.6
69.2
66.3
MAE
300.8
302.8
305.4
Energy
ENG03
MAPH
7.4
7.4
7.5
MAE
60.0
60.1
46.4
ENG04
MAPE
124.7
116.0
MAE
6.2
9.5
47.0
ENG05
MAPE
44.1
42.7
100.8
PHU01
FI
44.5
56.2
61.2
MAE
15.4
15.2
14.8
PHG02
MAPE
163.0
159.9
147.5
14.5
PHG03
MAPE
20.4
24.0
125.9
Physiology
Acc
90.5
62.9
60.9
PHU04
FI
92.7
83.6
64.4
Acc
38.8
51.8
72.5
PHUOS
FI
23.0
22.5
21.9
Acc
26,8
54.8
44,8
PHU06
FI
23.4
55.5
46.9
URGOL
MAPE
247.0
295.2
463.0
MAE
427.4
495.8
455.4
URG02
MAPE
1057.5
1253.5
1212.7
MAE
143.0
147.9
436.8
Urbanism
URG03
MAPE
132.5
142.8
617.0
Acc
47.5
44.5
54.7
URU04
FI
64.8
63.5
67.4
MAE
1655.8
1651.8
1662.8
URGO5
MAPE
174.0
175.2
174.4
Acc
90.0
63.8
29.3
MFU01
F1
90.1
71.2
43.2
Acc
95.8
84.0
36.5
Manufacturing
MFU02
Fl
94.8
87.5
35.2
Acc
47.8
48.7
25.3
MFU03
F1
61.0
66.6
66.7
Acc
89.8
87.6
56,3
RAU01
F1
82.9
80.6
45.9
Radar
Acc
51.9
51.3
17.7
RAU02
Fl
54.9
53.5
20.9
MAE
3.7
3.7
3.9
Math
MAG01
MAPE
656.5
606.9
849.7
35

---

Published as a conference paper at ICLR 2026
H DISCUSSION OF THE EVALUATION SETUP
TimeOmni is trained jointly across multiple domains. Its training data do not (and can hardly) cover all domains and tasks SciTS, given the inherent sparsity scientific time series. Tasks such as ENG04-05 and URG02-04 were evaluated in an out-of-domain setting, with detailed results provided in Appendix F While TimeOmni may not outperform specialised time-series forecasting models on all forecasting tasks, it is able to handle tasks such as imputation and QA that those models cannot solve, and it outperforms general purpose LLMs both performance and success rate. TimeOmni is introduced system intended to surpass baselines, but as an initial demonstration of how L.MSs might be extended to accommodate heterogeneous and complex scientific time series.
proof-of-conee purrrol-puppose architecture, not as an all-purpose model or
To further address concerns about fair comparison, we additionally finetuned two open-source mod- els, one image-text LLM and one time-series foundation model using the same training data as TimeOmni. Finetuning a text-only LLM is impractical in this setting, as converting time-series signals into text produces extremely long token sequences, making both tokenization and training prohibitively expensive. The results are shown in Tables 20 22
We first finetuned the image-text LLM, Qwen2.5VL-7B, with LoRA of rank 64. Results show that finetuning yields performance improvement on some tasks, but the gains are limited possibly because representing time series as images compromises numerical precision. For the ENG03 task, where the model fails to follow instructions in the zero-shot setting, finetuning improves instruction- following ability, although
than half of the samples still fail produce the required length. We further observed instability during finetuning and difficulty in maintaining balanced performance across tasks, potentially reflecting the limited capacity of image-based representations for time- series data.
We then fully finetuned TimeMoE, time series model specialised for forecasting. Although fine- tuning improves its performance on some of the forecasting tasks, finetuning does not enable it to handle tasks it could not previously address, such as imputation or QA.
Table Finetuning baselines on scientific time series data.
Owen2.5-VL.7B (zero-shot)
Qwen2.5-VL-7B (finetuned)
TimeMoE (zero-shot)
TimeMoE. (finetuned)
Domain
Tasks
Metric
TimeOmni
Acc
69.0
48.4
48.8
ASU01
73.5
2.9
MAPE
2.8
6.7
Astronomy
ASG02
Success Rate
100.0
1.5
2,4
Acc
87.8
27.5
ASU03
F1
72.8
11.3
10.0
EAU01
Acc
80.9
75.8
$2.8
Earth Science
Fl
82.5
80.6
68.1
EAG02
2.2
53.5
Success Rate
100.0
99.5
99.7
Ace
42,6
3.3
2.1
BILO1
F1
34.1
0.3
Bioacoustics
BIU02
Acc
83.4
81.0
82.7
Fl
51.2
22.9
18.1
BIU03
Acc
89.6
16.7
15.6
F1
89.2
4.8
MEU01
Acc
52.0
54.1
49.9
F1
65.2
36.4
0.9
Acc
48.4
0.0
73.4
MEU02
Meteorology
F1
38.8
0.0
MAE
3.0
5.5
2,9
2.9
2.9
MEG03
MAPE
37.5
31.1
36.0
Success Rate
100.0
0.2
100.0
100.0
MEU04
Acc
80.0
47.9
52.1
36

---

Published as a conference paper at ICLR 2026
Table 21: Finetuning baselines on scientific time series data (Cont.).
Qwenn25-VVL.7 (zero-shot)
Qwen2.5-VIL-78 (finctuned)
TimeMoE (zero-shot)
TimeMoR (finetuned)
Domain
SubTaske Tasks
Metric
TimcOmni
MAE
2.1
ECG01
MAPE
6.2
Success Rate
100.0
Economics
MAE
4.3
26.9
2.5
3.0
ECG02
MAPE
4.5
1.6
9.3
2.7
3.3
Success Rate
100.0
100.0
100.0
ECU03
Ace
96.4
81.9
66.7
Acc
67.2
50.0
50.0
76.6
0.0
0.0
43.0
18.8
NEU02
F1
35.7
2.4
14.7
MAE
10.0
20.
42.1
5.9
6.1
NEG03
MAPE
Success Rate
100.0
18.1
27.5
100.0
100.0
Neuroscience
MAE
2.1
53.5
135.1
749.2
Success Rate
100.0
1.9
46.9
NEU05
Acc
50.4
33.4
33.3
NEU06
Ace
73.0
13.7
35.6
FI
67.8
4.8
13.3
MAE
8.2
ENG01
MAPE
100.0
Success Rate
100.0
MAE
2.2
ENG02
MAPE
68.6
71.6
Success Rate
100.0
0.0
100.0
100.0
MAE
300.8
1841.8
469.0
294.7
Energy
ENG03
MAPE
7.4
Success Rate
100.0
33.0
100.0
100.0
MAE
60.0
0.4
703.7
10.4
17.6
ENG04
MAPE
Success Rate
100.0
9.1
4.6
100.0
100.0
MAE
6.2
2658.3
ENGGS
Success Rate
100.0
9.2
PHU01
H1
44.5
0.2
24.6
MAE
15.4
15.5
7.7
PHG02
MAPE
163.0
384.3
140.3
78.7
79.6
Success Rate
100.0
21.5
5.5
100.0
100.0
MAE
$4.7
PHG03
MAPE
20.4
950.8
492.9
Physiology
Success Rate
100.0
10.1
21.7
PHU04
Ace
90.5
43.7
50.0
92.7
49.6
PHUOS
Acc
38.8
87.4
89.6
23.11
2.5
Acc
26.8
41.3
41.4
PHU00
F1
23.4
14,4
15.0
37

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Published as a conference paper at ICLR 2026
Table 22: Finetuning baselines on scientific time series data (Cont.).
Qwen2.5-VI-7B (zero-shot)
Qwen2.5-VI-77 (finctuned)
TimeMoK (zero-shot)
TimeMol:
Domain
Tasks
Metric
TimeOmmi
(finctuned)
MAE
0.1
URG01
MAPE
247.0
686.4
204.4
260.1
Success Rate
100.0
4.7
0.0
100.0
100.0
MAE
427.4
2123.0
72.5
97.6
URGOZ
MAPE
1057.5
9328.6
55.4
94,4
Success Rate
100.0
17.7
100.0
100.0
Urbanism
MAE
198.0
37313
URG03
MAPE
132.5
137.4
5665.8
Success Rute
100.0
4.6
66.2
URU04
F1
64.8
58.5
0.0
MAE
1655.8
836.5
1274.2
691.5
492.2
URGOS
MAPE
116.3
89.3
89.5
70.2
Success Rate
100.0
16.9
98.4
100.0
100.0
MFU01
Ace
90.0
10.3
90.1
Manufacturing
MFU02
Acc
95.8
10.5
94,8
0.0
50.0
MFU03
F1
61.0
17.7
0.0
RAU01
Acc
89.8
19.9
19.9
82.9
6.7
6.7
Radar
RAU02
Acc
51.9
13.4
13.0
F1
54.9
5.2
4.7
MAE
3.7
2.4
2.7
Math
MAG0I
MAPE
656.5
$73.0
840.0
Success Rate
100.0
100.0
100.0
We would like to highlight that the key insight from benchmarking on SeiTS is the architectural their inability to support the diverse signals and task types
limitations of existing SciTS. As shown Fig. 4b the success rates and fallure analyses demonstrate that these are structural issues, which cannot be resolved merely through finetuning (as further confirmed by the results above).
38

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Published as a conference paper at ICLR 2026
I CASE EXAMPLE
In this section, we present several case examples from SciTS, For visualisation purpose, time series are plotted as image. Note that SciTS works on raw time series.
EEG signal forecasting (NEG03)
Predict the next time series points based the given data.
Time Series
25
20
15
10
5-
0
20
40
80 Number of Samples
60
100
120
140
Question:
Now need you to finish EEG Recordings (ALS Patient) forccasting task. The dataset involves an ALS Patient performing self-regulation of slow cortical potentials (SCPs) to control cursor. The sampling frequency of this dataset is 256 Hz. Please predict the next time series points given the picture above.
Sleep staging classification (NEU06)
Determine which stage of sleep subject is in from EEG data.
Time Series
50
mnl M M / prrnp
0-
-50
500
1000
1500
2000
2500
3000
Number of Samples
Question:
Now, I need your assistance complete sleep stage classification task. This is single- channel EEG data from a sleeping person. Based on this data, please determine which sleep stage the person is in. It is known that the provided picture corresponds to one of the following categories:
Choices:
I. wakefulness stage
2. NI stage
3. N2 stage
4. N3 stage
5. REM stage
Answer:
This data is classified wakefulness stage.
39

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Published as a conference paper at ICLR 2026
ECG anomaly detection (PHU04)
Determine whether subjects exhibit abnormal conditions given their ECG data. Time Series
0.025
0.000
-0.025
-0.050
00.055
10
30 40
50
Number of Samples
Question:
Please determine whether an ECG anomaly event has occurred the provided picture. The following data represents ECG (Electrocardiogram) signals, which record the electrical activity of the heart during each heartheat and as important tool for diagnosing heart diseases. Using a signal sampled at a frequency of 500 Hz, we can determine whether abnormalities are present in the human body. If so, please indicate whether it includes Normal Anomaly Points.
Answer:
Based on the given information, this time series includes Normal Point.
Pedestrian flow forecasting (URG02)
Predict the future pedestrian flow based on past data. Time Series
2000
1500
1000
500
50
100
150
200
250
Number of Samples
Question:
Now need you finish Pedestrian activity forecasting task. The Melbourne Pedestrian dataset contains pedestrian count data from 10 sensor locations in the City of Melbourne, Australia, for the entire year of 2017. The dataset is designed to facilitate urban planning by analysing pedestrian activity across different times and locations, with each class representing specific sensor placement site. The frequency is one hour. Please predict the next time series points given the picture above.
40

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Published as a conference paper at ICLR 2026
Comprehensive electricity imputation (ENGO5)
Completes the electricity data of a certain city. Time Series
5500
4500
4000
3500
3000
50
100
150
200
250
Number of Samples
Question:
Now need you to finish an Aus. Electricity Demand Imputation Task. The Aus. The Electricity Demand Dataset provides half-hourly measurements of electricity demand for Victoria, Australia, throughout the year 2014. The frequency is 30 min. Please give full time series with 7 missing values imputed. The missing values in the picture are represented by vertical lines.
Gravitational wave event localisation (ASU02)
Analyse given gravitational wave data to determine when gravitational waves occur.
Time Series
0.5
0.0
-0.5
-1.0
10000
20000
30000
40000
50000
60000
Number of Samples
Question:
Please determine whether a Gravitational Wave event has occurred in the provided picture. If please specify the index of the starting time point of the event.
Answer:
A Gravitational Wave event was detected in this time-series signal. The starting time index is 24729.
41

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Published as a conference paper at ICLR 2026
Earthquake event localisation (EAU01)
Determine the time points of occurrence of p-wave and s-wave in the three-dimensionl seismic waves. Time Series
50
550
1000
2000
4000
5000
6000
50
0
-50
0
1000
2000
3000
4000
5000
6000
100
1000
2000
3000
4000
5000
6000
Number of Samples
Question:
Please determine whether an Earthquake event has occurred in the provided picture. If so, please specify the starting time point indices of the P-wave and S-wave in the event.
Answer:
An Earthquake event was detected in this time-series signal. The starting time index of the
P-wave is 800, and the S-wave is 1451.
Stock MCQ (ECU03)
Analyse the given time series data solve the multi-choice question.
Time Series
460
455
450
50
100
150
200
250
300
350
Number of Samples
Question:
Now need you to finish Stock QA task. This data is used to analyse which of the four options, A. C, and correct. Please answer the question based on the given picture.
Choices:
A. The consistent increase in stock price post-news indicates positive market reception financial report, suggesting bullish sentiment
the investment recommendations provided among investors.
B. The price movement shows volatility without any clear trend, indicating investor uncertainty regarding UNH2019s future performance despite the positive news.
C. The stock price fluctuations in the days following news publication demonstrate a wcak correlation with changes in earnings estimates, implying that investors were not influenced by the market data provided.
The stock price decreased significantly after the news announcement, suggesting that the market reacted negatively to the suggested investment in UNH.
Answer:
Option is correct.
42


Reviews:
[{"review_id": "9kEyBudvSm", "reviewer": "Reviewer_P26w", "summary": "This paper introduces SCITS (Scientific Time Series Understanding and Generation) — a comprehensive benchmark encompassing 12 scientific disciplines, 43 tasks, and over 50,000 instances — designed to evaluate the capability of large language models (LLMs) in processing scientific time series data. The authors observe that existing multimodal LLMs either encode time series as text (leading to excessive sequence lengths) or as images (losing numerical precision). To bridge this gap, the paper proposes TimeOmni, an LLM-compatible framework that explicitly models temporal dynamics through patch experts and adaptive routing, enabling both understanding and generation of time series while remaining compatible with general LLM training. Extensive benchmarking across 17 models—including GPT-5, Gemini-2.5, Qwen3, and Moirai—demonstrates that TimeOmni consistently outperforms both general LLMs and specialised time-series models across most scientific domains.", "questions": "1. Given that the router dynamically selects patch experts, what is the computational overhead compared to standard LLM inference? Is the training cost comparable to multimodal extensions (e.g., audio or vision encoders)?\n2. How does TimeOmni perform under few-shot or domain-specific fine-tuning? Could small-scale adaptation bridge the performance gap between open-source and closed-source LLMs?\n3. Could the authors provide quantitative ablation results on the router and patch-reprogramming components to confirm their necessity?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The introduction of SCITS represents a major step forward for LLM-based scientific data understanding. The benchmark’s diversity—covering astronomy, neuroscience, meteorology, physiology, and more—significantly broadens the scope beyond traditional forecasting or anomaly detection benchmarks.\n2. The dual emphasis on understanding (classification, QA, anomaly detection) and generation (forecasting, imputation, synthesis) is novel, establishing SCITS as arguably the first unified evaluation for both reasoning and signal generation.\n3. The manuscript is clearly written, logically structured, and well-illustrated.\n4. TimeOmni not only achieves full task coverage but also top-1 average ranking across nearly all disciplines, demonstrating both robustness and generality.", "weaknesses": "1. The paper briefly mentions recent works like Time-LLM and ChatTS, but deeper discussion of conceptual differences and computational efficiency trade-offs would help position TimeOmni more clearly in the landscape.\n2. The paper evaluates models in a zero-shot setting only. While this fairly tests generalisation, it leaves open the question of how much performance could improve with lightweight adaptation, e.g., LoRA or task-specific finetuning.\n3. Although the patch-expert routing and reprogramming modules are key innovations, the paper does not include ablations isolating their contributions. Quantifying improvements from each would strengthen the architectural claims.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "oe2h5VBF08", "reviewer": "Reviewer_4z7J", "summary": "The paper titled “SciTS: Scientific Time Series Understanding and Generation with LLMs”  introduces SciTS, a new benchmark for scientific time series understanding and generation, spanning a number of disciplines, tasks and data with diverse signal characteristics. The authors benchmark 17 models, revealing that general-purpose LLMs often show better generalization than specialized time series models, but their performance is limited when time series are converted to text (due to long sequences) or images (due to loss of precision). To address these challenges, the paper proposes TimeOmni, an LLM-based framework that explicitly models temporal dynamics and supports both time series understanding and generation, achieving the top rank on the challenging SciTS benchmark.", "questions": "na", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The benchmark provided is pretty exhaustive for scientific time series. It consists of 52,056 instances spanning 43 domain-specific tasks across 12 scientific disciplines. It has has diversity types of data including both univariate and multivariate. It also includes various task types such as anomaly detection, classification, multiple-choice question answering (MCQ), event localization, forecasting, imputation, and synthesis\n\nThe proposed TimeOmni demonstrates its effectiveness by achieving the highest overall ranking on the challenging SciTS benchmark, underscoring the advantage of its approach.", "weaknesses": "The paper is primarily a benchmark contribution (SciTS), with the proposed TimeOmni methodology being a secondary, and arguably incremental, focus. While the benchmark is vast, this heavy reliance on data creation means the paper's core scientific novelty may be perceived as low, as the methodological innovation is not groundbreaking.\n\nThe creation of the SciTS benchmark largely involves the collection and curation of existing open-source datasets and data from scientific domain websites, alongside some numerical simulation methods. While the combination, annotation, and unifying under a prompt-based format are novel contributions , the underlying raw time series signals themselves are largely drawn from pre-existing sources.\n\nThe proposed TimeOmni framework is an adaptation of existing LLM componentsFor instance, it uses a Patch Reprogramming module which is a concept previously presented in Time-LLM (Jin et al., 2024),  the router and patch family are basically selective resizing. \n\nThe title, \"SCITS: SCIENTIFIC TIME SERIES UNDERSTANDING AND GENERATION WITH LLMS,\" is misleading. The title should use the word benchmark because that is what it is. Potential suggestion:\n\"SCITS: A NEW BENCHMARK FOR SCIENTIFIC TIME SERIES UNDERSTANDING”", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "F9JOYgd1Zz", "reviewer": "Reviewer_pjb9", "summary": "The paper collects a comprehensive set of scientific related multimodal time series datasets, spanning various tasks including forecasting, classification, anomaly detection, etc. It benchmarks text-only LLMs, multimodal LLMs, and unified time series models and addresses the limitations of existing approaches by its proposed TimeOmni. TimeOmni fine-tunes an LLM by aligning time series representations and text representations, with separate output heads for different tasks. Extensive experiments across the benchmark show that TimeOmni achieves strong performance relative to all three categories of existing methods.", "questions": "1. Have the authors explored RL training after supervised fine-tuning?\n\n2. For multivariate time series with dimensions up to 58 in Neuroscience, would the current representations make the input length very long? \n\n3. How to compute the scores of a task if a part of samples fail this task? For example, did the authors impute zeros to compute MAPE for those failed samples?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper contributes a large-scale multimodal time series benchmark covering diverse tasks and domains.\n\n2. The paper conducted broad evaluation comparing both text-only LLMs, multimodal LLMs, and unified time series models.\n\n3. The authors propose a new model that aligns time series and text, and show through extensive experiments that the model outperforms all three types of existing methods.", "weaknesses": "1. Is TimeOmni trained and evaluated per domain, or is it trained jointly across all domains and tasks? The compared models, even for those open-source models with the same scale, are evaluated in a zero-shot setting, so it seems unfair for those models as TimeOmni has been fine-tuned on the target domains. I may have missed this part, but have the authors tried any out-of-domain testing?\n\n2. The current design does not support dynamic-length generation, which limits flexibility and scalability compared to sequence decoders that can have variable-length outputs.\n\n3. Apart from per-domain performance, are there any results on per-task performance? For example, how does TimeOmni compare with current time series models on the forecasting task? Practitioners may not care about having a general model that can handle all the tasks but more on the performance for a specific task of their interest.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "JCaWBlp28r", "reviewer": "Reviewer_5o2d", "summary": "This paper introduces SciTS, a large-scale benchmark for evaluating the ability of LLMs to understand and generate scientific time series across 12 domains, 43 tasks, and over 52k instances. It further proposes TimeOmni, a modular framework that integrates explicit temporal encoding, router-based patch experts, and patch reprogramming for unifying diverse scientific signals within LLM architectures.", "questions": "1. How is normalization handled across domains with very different frequency/time scales?\n2. Were all non-TimeOmni baselines strictly zero-shot? If so, could you include a variant of TimeOmni under the same condition?\n3. How stable and interpretable is the router-based patch expert mechanism during inference?\n4. Do you plan to release detailed data provenance and licensing information to ensure benchmark sustainability?\n5. Could the framework extend to spatio-temporal or higher-dimensional scientific data (e.g., radar or 3D simulations)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. SciTS fills a gap by offering a unified high-quality evaluation suite for scientific time series\n2. The benchmark includes 17 baselines across modalities with consistent metrics, which is good for cross-domain generalization and model behavior.", "weaknesses": "1. TimeOmni uses fine-tuning while others are evaluated purely zero-shot, which would impact strict comparability. An ablation without fine-tuning would strengthen the claim.\n2. No ablation for key modules, like router, patch reprogramming, and expert families.\n3. It remains unclear how TimeOmni handles very long or high-frequency sequences beyond benchmark scales.\n4. Benchmark provenance and overlap with LLM pre-training corpora are not fully documented.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)