Exploring Representations and Interventions in Time Series Foundation Models

Authors: Michał Wiliński, Mononito Goswami, Nina Żukowska, Willa Potosnak, Artur Dubrawski
Primary area: learning on time series and dynamical systems
Keywords: Time Series Foundation Models, Model Steering, Interpretability, Pruning

Abstract:
Time series foundation models promise to be powerful tools for a wide range of applications. However, their internal representations and learned concepts are still not well understood. In this study, we investigate the structure and redundancy of representations across various TSFMs, examining the self-similarity of model layers within and across different model sizes. This analysis reveals block-like redundancy in the representations, which can be utilized for informed pruning to improve inference speed and efficiency. Additionally, we explore the concepts learned by these models—such as periodicity and trends—and how these can be manipulated through latent space steering to influence model behavior. Our experiments show that steering interventions can introduce new features, like adding periodicity or trends to signals that initially lacked them. These findings underscore the value of representational analysis for optimizing models and demonstrate how conceptual steering offers new possibilities for more controlled time series modeling.

Decision: reject
Reviewer scores: 6, 6, 6

Reviews and discussion:
<thread>
creation date:
1730375578002

thread_type:
official_review

<thread_object>
creator: reviewer_mrnr

<content>
summary:
In this paper, the author investigates the structure and redundancy of representations across various time series foundation models, examining the self-similarity of model layers within and across different model sizes. This analysis reveals block-like redundancy in the representations, which can be utilized for informed pruning to improve inference speed and efficiency. Additionally, the autjor explores the concepts learned by these models—such as periodicity and trends—and how these can be manipulated through latent space steering to influence model behavior. These findings underscore the value of representational analysis for optimizing models and demonstrate how conceptual steering offers new possibilities for more controlled and efficient time series analysis with TSFMs.

strengths:
The starting point of this article is very meaningful, being at a time when TSFMs are emerging, it is crucial to explore more efficient, interpretable, and controllable models. Additionally, some experimental findings in the article provide guidance for future TSFM architecture design. Moreover, the figures and tables in this paper are also aesthetically pleasing.

weaknesses:
I have no objections to the content of this article. My only concern is that it reads more like a technical report, lacking an overall logical structure. Many figures and text sections appear isolated, which could make it difficult for beginners to read and understand. I suggest the authors expand the Introduction section to provide readers with a more comprehensive overview (currently only half a page) and adjust the layout of the figures to accurately correspond with the text content.

questions:
* **Q1**: I am a bit unclear about the concept of "block" in the article. Does it refer to some adjacent layers? What does it mean to prune Block3 of MOMENT-large at line 426?

* **Q2**: Can you provide a more detailed explanation on how to control the generation of time series? Does this involve modifying some parameters of the model?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730634026887

thread_type:
official_review

<thread_object>
creator: reviewer_qt6a

<content>
summary:
This work investigates the internal representations of Time Series Foundation Models (TSFMs), revealing block-like redundancy in their representations. It leverages this redundancy for informed pruning to enhance inference speed and efficiency without compromising accuracy. The study also explores learned concepts like periodicity and trends, demonstrating the potential of latent space steering to manipulate these concepts and influence model behavior, introducing new features to time series signals. The findings highlight the importance of representational analysis for optimizing TSFMs and open new avenues for controlled time series analysis.

strengths:
1 By comparing representational similarity between layers of the model, this work provides a new method for analyzing TSFMs. Systematic probing of TSFMs through representational similarity identifies redundant representations suitable for model pruning.

2 The work effectively reduces model size and improves inference speed while maintaining accuracy. It introduces the ability to steer model predictions along conceptual directions, influencing outputs in targeted ways.

3 The study provides comprehensive analytical experiments, verifying that steering interventions can introduce new features into signals, such as periodicity and trends. Moreover, it is not limited to a single model but spans multiple TSFMs, enhancing the generality of the findings.The use of open-source models and data facilitates community reproduction and further research.

weaknesses:
1 Although a pruning strategy is proposed, the effectiveness of reducing computational resource consumption for extremely large models like TimeGPT has not been fully validated.

2 The selection of the parameter α in concept steering may require fine-tuning, increasing the complexity of model application. Have you explored automated methods for selecting the parameter α, or can you provide guidance on choosing appropriate α values for different dataset and models.

3 The study primarily focuses on forecasting and does not explore the generalization of the findings to other time series tasks such as anomaly detection or classification.

4 The computational overhead of calculating and applying steering matrices in real-time scenarios is not addressed, which could be significant for large-scale applications.

questions:
Refer to weaknesses
</content>
</thread_object>

</thread>
<thread>
creation date:
1731042114399

thread_type:
official_review

<thread_object>
creator: reviewer_kpxg

<content>
summary:
This paper aims to understand the internal representations of time series foundation models (TSFM). By analyzing the self-similarity of the model layers, this paper reveals block-like redundancy in the structure that can be used for model pruning. This representational analysis also shows that the conceptual steering  potentially enables more controllable analysis for TSFM.

strengths:
1.	The main aim of this paper is to address the gap in understanding the underlying mechanisms and learned representations of TSFMs, which remain largely unexplored. The representational analysis examined the representational similarity based on Centered Kernel Alignment (CKA), identified concepts, and localized them to specific hidden states. The representation redundancy found in this analysis was used to prune the model, which reduces the model size as well as improves the inference speed. 
2.	The findings in this paper help us understand the TSFMs, and the knowledge can potentially be used to improve their capabilities. For example,  the predictions can be steered along the conceptual directions using synthetic time series.

weaknesses:
1.	The analysis is conducted using many existing techniques, which are not specifically designed for time series models. There may be room for improvement by introducing novel techniques specifically designed for TSFMs. 
2.	Section 3.2 is a huge subsection that contains many paragraphs. These paragraphs can be further grouped into subsubsections for better viewing.

questions:
1. Applying concept steering interventions across all tokens is necessary to achieve the intended steered concept output compared to applying concept steering interventions to a single token. What is the main purpose of concept steering? Is it controllable to achieve some target objectives?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732244196794

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Global Response

comment:
Dear Reviewers and Area Chair,

We sincerely appreciate the time and effort you have dedicated to reviewing our paper. Below, we summarize the strengths of our work as identified by the reviewers:
1. **Meaningful Contributions:** Reviewer MRNR remarked that our work is “very meaningful, being at a time when TSFMs are emerging, it is crucial to explore more efficient, interpretable, and controllable models.” Our work not only addresses timely challenges in an emerging area of research but also demonstrates practical utility, as noted by reviewers kpxg and QT6A, who remarked that our methods “effectively reduce model size and improve inference speed while maintaining accuracy.” We are encouraged that all reviewers (kpxg, QT6A, and MRNR) consider our work a "good" contribution, as reflected in their ratings.  
2. **Novel Methods:** Reviewer kpxg recognized as a strength that our work “addresses the gap in understanding the underlying mechanisms and learned representations of TSFMs, which remain largely unexplored”. Similarly, reviewer GT6A recognizes the novelty of our work and mentions that “By comparing representational similarity between layers of the model, this work provides a new method for analyzing TSFMs” and “It introduces the ability to steer model predictions along conceptual directions, influencing outputs in targeted ways.”:

3. **Generalizable and Reproducible Findings:** Reviewer QT6A appreciated that our work “is not limited to a single model but spans multiple TSFMs, enhancing the generality of the findings. The use of open-source models and data facilitates community reproduction and further research.” Additionally, reviewer kpxg noted that our findings “help us understand TSFMs and can potentially be used to improve their capabilities,” while MRNR highlighted their utility in providing “guidance for future TSFM architecture design.”

While these strengths are encouraging, your thoughtful questions and suggestions helped us identify key areas for improvement. We summarize the revisions and responses below:

1. **Improve readability:** Reviewers kpxg and MRNR highlighted the need for better organization, particularly in Section 3.2, and a more comprehensive introduction. In response, we made substantial revisions to improve the clarity, structure, and flow of the paper.
2. **Computational Overhead of Steering:** Reviewer QT6A expressed concern over the computational overhead of computing and applying steering matrices. Our analysis, detailed in the response to QT6A, demonstrates that steering introduces minimal computational overhead while enabling effective control of model predictions.
3. **Experiments on tasks beyond forecasting:** Reviewer QT6A also noted the need for broader experimentation. In the revised manuscript, we added pruning experiments on classification and anomaly detection, in addition to imputation/reconstruction, and forecasting. We also added steering experiments for classification on a real-world ECG dataset in addition to imputation/reconstruction, and forecasting.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732486344907

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Author Reviewer Discussion

comment:
Dear Reviewers and Area Chair,

We hope you are doing well! 

We really appreciate your time and feedback! As the author-reviewer discussion period draws to end, we were hoping to engage with you to address any remaining questions or concerns. 

We have made several changes to the paper in response to your feedback as summarised in the global response. 

Thanks for helping us improve our paper and looking forward to hearing from you.

Authors
</content>
</thread_object>

</thread>


Metareview:
metareview:
This work examines the internal representations of Time Series Foundation Models (TSFMs), identifying block-like redundancy within these representations. By exploiting this redundancy, the authors propose an informed pruning strategy that enhances inference speed and efficiency without sacrificing accuracy. Additionally, the study investigates learned concepts such as periodicity and trends, demonstrating the potential of latent space steering to manipulate these concepts and influence model behavior. This approach enables the introduction of new features to time series signals, offering a novel way to guide model predictions in specific conceptual directions.  

While the method effectively reduces model size, improves inference speed, and maintains accuracy, a significant concern is that the paper lacks a cohesive logical structure and reads more like a technical report. Furthermore, it fails to offer any substantial new insights to the research community.