Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series

Authors: Yuxiao Hu, Qian Li, Dongxiao Zhang, Jinyue Yan, Yuntian Chen
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Time Series, Large Language Models, Context-Alignment

Abstract:
Recently, leveraging pre-trained Large Language Models (LLMs) for time series (TS) tasks has gained increasing attention, which involves activating and enhancing LLMs' capabilities. Many methods aim to activate LLMs' capabilities based on token-level alignment, but overlook LLMs' inherent strength in natural language processing — their deep understanding of linguistic logic and structure rather than superficial embedding processing. We propose Context-Alignment (CA), a new paradigm that aligns TS with a linguistic component in the language environments familiar to LLMs to enable LLMs to contextualize and comprehend TS data, thereby activating their capabilities. Specifically, such context-level alignment comprises structural alignment and logical alignment, which is achieved by Dual-Scale Context-Alignment GNNs (DSCA-GNNs) applied to TS-language multimodal inputs. Structural alignment utilizes dual-scale nodes to describe hierarchical structure in TS-language, enabling LLMs to treat long TS data as a whole linguistic component while preserving intrinsic token features. Logical alignment uses directed edges to guide logical relationships, ensuring coherence in the contextual semantics. Following the DSCA-GNNs framework, we propose an instantiation method of CA, termed Few-Shot prompting Context-Alignment (FSCA), to enhance the capabilities of pre-trained LLMs in handling TS tasks. FSCA can be flexibly and repeatedly integrated into various layers of pre-trained LLMs to improve awareness of logic and structure, thereby enhancing performance. Extensive experiments show the effectiveness of FSCA and the importance of Context-Alignment across tasks, particularly in few-shot and zero-shot forecasting, confirming that Context-Alignment provides powerful prior knowledge on context. The code is open-sourced at https://github.com/tokaka22/ICLR25-FSCA.

Decision: accept (poster)
Reviewer scores: 6, 6, 6, 6

Reviews and discussion:
<thread>
creation date:
1730212930745

thread_type:
official_review

<thread_object>
creator: reviewer_n4cb

<content>
summary:
The authors propose a new method for adapting LLMs for time-series analysis tasks. They test on both time-series generation (forecasting) and time-series classification. Their paper proposes the use of Dual-Scale Context-Alignment GNNs which are used to perform context alignment of the time-series and text tokens/patches.
They combine this strategy with example prompts which they are calling (Demonstration Examples Prompt Technique) and show that using DECA and the dual scale context alignment improves the performance of LLM’s compared to existing LLM benchmarks for time-series analysis.

strengths:
1. Their method of context alignment seems new and interesting
2. The course and fine-grained context alignments are logical and fit well in combination with patching which is a very popular embedding strategy for time-series data
3. They several different experiments using different tasks and experiment settings to show their model performs at a high level consistently

weaknesses:
1. This paper focuses on enhancing LLM’s for time-series analysis and it takes a pretrained GPT model and compares it to other LLM-based approaches. This means this approach is ultimately a pretrained model approach (with the LLM being a pretrained model). The authors need to be comparing to other pretrained models and not just LLM based ones. Since its not being used for text generation-based tasks there’s no reason to limit this evaluation to only LLM based approaches and there are many pretrained models which follow the same experimental method.
2. The proposed method hinges heavily on the use of GNN’s and while they do run an ablation study, from what I understand they don’t compare other network architectures for performing the context alignment. Given that the GNN is a major part of the proposed implementation, an ablation showing other networks as course and fine-grained context aligners is necessary. For example, Linear layers, CNNs and self-attention could be a good start.
3. I would like more details on the training, their method does add a layer of complexity in adapting LLM’s for time-series analysis and the amount of added compute for finetuning these models (time and device) should be clearly stated.

questions:
1. While your methodology considers the strengths of GNN’s as context aligners, I would like to see the choice of GNN’s to be validated experimentally as opposed to other options
2. In my opinion the terminology is unnecessarily complicated, and it takes away from the impact of the paper. One key example is “VCA w/o DSCA-GNNs” used in table 1. It seems to me that this is simply just prompting since there are no context aligners used. It may be more clear if you denote VCA as context alignment without DSCA-GNNs and mark in table 1 VCA + DSCA-GNNs. 
 3. What is the difference between “(DECA) DEMONSTRATION EXAMPLES BASED CONTEXT-ALIGNMENT” and few-shot prompting as in NLP. It seems like this term was invented to enhance the complexity of the paper but at the cost of reader understanding, especially if that reader has an NLP background.
4. How does the prompt effect the context alignment? Are there some prompts that harm context alignment? Since prompting is a key component of this paper, it would be useful to know how their new method performs with different prompt types.
5. Since DECA involves using examples for context alignment a fair comparison against other baselines would be the VCA with the GNN as a context aligner. I think that should be included beside DECA in the results chart
</content>
</thread_object>

</thread>
<thread>
creation date:
1730701480889

thread_type:
official_review

<thread_object>
creator: reviewer_ofgl

<content>
summary:
This paper introduces Context-Alignment, a novel paradigm for enhancing the capabilities of LLMs in time series tasks. The authors argue that leveraging LLMs' strengths in natural language processing, particularly their understanding of linguistic logic and structure, is key to improving their performance on time series data. They propose a Dual-Scale Context-Alignment Graph Neural Networks (DSCA-GNNs) framework that aligns time series data with linguistic components, enabling structural and logical alignment. This framework is used to develop Demonstration Examples based Context-Alignment (DECA), which integrates seamlessly into pre-trained LLMs to enhance their awareness of logic and structure. Extensive experiments across various time series tasks, including forecasting and classification, demonstrate DECA's effectiveness, especially in few-shot and zero-shot scenarios, highlighting the importance of context alignment in activating and enhancing LLMs' potential in time series applications.

strengths:
The paper strengthens the representation of time series data through GNNs, addressing a gap in prior research on applying LLMs to time series tasks. The method shows promising results and could inspire future work in this area.

weaknesses:
- The paper's incorporation of GNNs is a functional approach, yet it overlooks a comparison of modeling time consumption, which is a critical aspect of efficiency that should be measured against baselines that do not employ this method.
- Additionally, while the paper presents a generalized embedding technique, further validation across a broader range of time series scenarios is needed to establish its robustness. Testing the method in other contexts, such as time series anomaly detection and imputation, would strengthen the claims of its effectiveness.

questions:
I have raised several concerns within the weaknesses. Please address the issues I've mentioned there.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730724059394

thread_type:
official_review

<thread_object>
creator: reviewer_2n38

<content>
summary:
This paper aims at addressing the alignment of Time series not just on the token level as done by previous works but at a level that enable LLMs to contextualize and comprehend TS in the same manner as it does for natural language. To this aim, the authors proposed Dual-Scale Context-Alignment GNNs that achieves context level alignment comprising structural alignment and logical alignment thereby activating LLMs’ potential capabilities in time series tasks.

strengths:
-  The paper addresses an important question of context alignment which promises better use of LLMs in the time series domain.
- The idea of using GNNs to introduce context alignment brings novelty to this work.

weaknesses:
- Since the authors use GNNs for context alignment, a befitting diagram showing the nodes and edges would have made it easier to follow the text.
- Some typos here and there in the paper.
- Datasets not described clearly in the experimental setup.

questions:
- I am not completely sure if averaging the error metrics across different prediction lengths is the best way to report and compare the results.
- Is there a justification as to why this averaging approach is adopted?
</content>
</thread_object>

</thread>
<thread>
creation date:
1731241249131

thread_type:
official_review

<thread_object>
creator: reviewer_dkrj

<content>
summary:
This work aims at leveraging and improving LLMs for time series tasks. Specifically, the authors propose context alignment, a technique that utilizes dual-scale GNNs in addition to the basic LLM architectures that helps LLM comprehend time series data. Few-shot prompting techniques in regular LLMs are also used in the time series design. Through various experiments on different time series benchmarks, the authors show the performance advantage of the proposed method over baseline models.

strengths:
This paper is tackling an interesting problem of leveraging and improving pretrained LLMs to do time series tasks. The authors explored a diverse set of benchmarks and baselines and showed a superior performance of the proposed method.

weaknesses:
This work motivates and explains the advantage of the proposed method by LLMs' deep understanding of linguistic logic and structure rather than superficial embedding processing. However, such explanations lack support from experiments. More analysis can be included on the LLM side. For example, does an inferior LLM basic architecture (either old design or small model sizes) or a badly trained LLM (undertrained or untrained) lead to a bad time series performance?


This work freezes most of the LLM structure and tunes dual-scale context-alignment GNNs between Transformer layers. There seems to be lacking analysis on the effect of fully tuning the attention and feed-forward layers as well, and the subsequent necessity of the dual-scale context-alignment GNNs in that case. 


One of the interesting attributes of LLMs is the scaling effect (e.g., on model sizes, training data sizes, few-shot prompting amounts). The scaling aspect of LLMs and the proposed DECA seems to be unknown in the context of time series.


The writing can be improved for clarity, e.g., by providing qualitative examples besides key equations, especially in the few-shot prompting part, and adding an algorithm table.

questions:
N/A
</content>
</thread_object>

</thread>
<thread>
creation date:
1732281664797

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Background & Challenges & Distinctions & Summary of revisions

comment:
We appreciate all reviewers for recognizing the contribution of our paper. In particular, reviewers 2N38, oFgL, and N4CB for recognizing the novelty of our paper, with N4CB further affirming logical model design. Following our supplementary experiments and responses, reviewers oFgL and N4CB have improved their overall assessments. Our thanks go to all reviewers and the AC for their support.

We summarize the research background, challenges, and key distinctions of our method below, then discuss each comment specifically in the reviewers' responses.

**Research Background:** Time series (TS) datasets span multiple domains such as medical, industrial, transportation, power, etc. Thus, there is a critical need for models like LLMs that can generalize across diverse domains. Recent research, including our method, aims to efficiently use pre-trained LLMs for time series tasks. Particularly, GPT4TS [1] has proven, both theoretically and empirically, that LLMs possess generic knowledge useful for these tasks.

**Challenges:** The significant differences between the training text data for LLMs and TS data hinder the effective activation of LLM capabilities in TS tasks.

**Distinction from Mainstream Token-Level Alignment Methods:** Token-alignment methods utilize a vocabulary and employ various techniques to align TS inputs with vocabulary words that describe temporal features, such as rise, fall, periodic, steady, short, long, and so on, facilitating comprehension by LLMs. Differing from these methods, we leverage the deep understanding of linguistic context by LLMs, and inspired by insights from the field of linguistics, to propose the Context-Alignment (including logical alignment and structural alignment). Context-Alignment aligns TS with a linguistic component to enable LLMs to contextualize and comprehend TS data, thereby activating their capabilities.

**Distinction from Directly Enhancing LLMs' Capabilities on TS:** We argue that without LLMs understanding TS data, enhancements are less interpretable and less effective. Thus, we first suggest a two-step route: activate, then enhance. Our Vanilla Context-Alignment (VCA) activates LLM capabilities through Context-Alignment, while our final method, DECA, extends VCA with Few-shot Prompting to boost performance, following our proposed steps scheme.

**Summary of revisions:** Following reviewer feedback, we primarily made the following modifications:

1. Renamed DECA to FSCA (Few-Shot Prompting based Context-Alignment) as suggested by Reviewer N4CB.

2. Added qualitative examples, algorithm tables, and a schematic diagram of the graph structure in Appendices E,F, as recommended by Reviewers Dkrj and 2N38.

3. Incorporated additional experiments and analyses in Appendix G, focusing on computational efficiency, comparisons with TS foundation models, and ablation studies such as alternative network structures to GNNs, based on broader reviewer suggestions.

[1] Zhou T, Niu P, Sun L, et al. One fits all: Power general time series analysis by pretrained lm[J]. Advances in neural information processing systems, 2023, 36: 43322-43355.
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper proposes a novel approach to leveraging LLMs for time series tasks by aligning time series data to linguistic contexts through dual-scale context-alignment GNNs (DSCA-GNNs). The authors validate their method with extensive experiments across forecasting, classification, and anomaly detection, demonstrating improved performance over several baselines, particularly in few-shot and zero-shot scenarios.

Strengths:
The paper tackles an interesting problem of adapting LLMs for time series tasks. The proposed context alignment induces "structural and logical alignment". The paper includes a wide range of experiments with meaningful results. 

Weaknesses:
Despite its strengths, the paper has several limitations. It lacks sufficient comparisons to non-LLM-based pretrained models in its original submission, which was mostly addressed during the rebuttal. 

Decision:
Based on strong empirical performance thorough revisions during the rebuttal, I recommend acceptance. The contributions set a strong foundation for future work in integrating LLMs with time series data.