Causal Analysis for Time Series Foundation Models

• URL: http://arxiv.org/abs/2608.24303v1
• Date: Tue, 25 Aug 2026 09:30:47 GMT
• Title: Causal Analysis for Time Series Foundation Models
• Abstract summary: We investigate how biases and failure modes of time series foundation models can be identified before deployment.<n>We apply our causal analysis framework to Chronos-2 and TimesFM-2.5 and test them across six distinct time series patterns.<n>The results also indicate a bias in both models towards overestimating persistence, sudden failures for both models against the regime switch pattern and failure for TimesFM-2.5 against the energy-release pattern.
• Score: 2.601570325792829
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transitioning from bespoke time series models towards time series foundation models changes the relationship of model and application from one-to-one to one-to-many. This shift introduces concentration risk as many, potentially high-risk, forecasting applications are exposed to the same biases and failure modes of a single time series foundation model. At the same time, this centralization allows for economies of scale in model development and validation. In this study we investigate how biases and failure modes of time series foundation models can be identified before deployment. We propose a causal analysis framework to investigate the ability of a time series foundation model to preserve time series patterns. To achieve this, we intervene on parameterized synthetic time series generators and measure the corresponding change in model output under ceteris paribus conditions. We apply our causal analysis framework to Chronos-2 and TimesFM-2.5 and test them across six distinct time series patterns. We find safe configurations for trend and harmonic oscillation patterns. The results also indicate a bias in both models towards overestimating persistence, sudden failures for both models against the regime switch pattern and failure for TimesFM-2.5 against the energy-release pattern. Our review of the original works for both models indicates that the findings might be explained by the data used for pretraining. We conclude our study with suggestions for further model development, recommendations for application-specific model selection, and a discussion of limitations and further research directions.

Related papers

• Understanding the Implicit Biases of Design Choices for Time Series Foundation Models [90.894232610821]
  Time series foundation models (TSFMs) are a class of potentially powerful, general-purpose tools for time series forecasting and related temporal tasks.<n>Their behavior is strongly shaped by subtle inductive biases in their design.<n>We show how these biases can be intuitive or very counterintuitive, depending on properties of the model and data.
  arXiv  Detail & Related papers  (2025-10-22T04:42:35Z)
• ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting [54.57031153712623]
  ARIES is a framework for assessing relation between time series properties and modeling strategies.<n>We propose the first deep forecasting model recommender, capable of providing interpretable suggestions for real-world time series.
  arXiv  Detail & Related papers  (2025-09-07T13:57:14Z)
• Spatial Reasoning with Denoising Models [49.83744014336816]
  We introduce a framework to perform reasoning over sets of continuous variables via denoising generative models.<n>For the first time, that order of generation can successfully be predicted by the denoising network itself.<n>Using these findings, we can increase the accuracy of specific reasoning tasks from 1% to >50%.
  arXiv  Detail & Related papers  (2025-02-28T14:08:30Z)
• Retrieval-Augmented Diffusion Models for Time Series Forecasting [19.251274915003265]
  We propose a Retrieval- Augmented Time series Diffusion model (RATD) RATD consists of two parts: an embedding-based retrieval process and a reference-guided diffusion model. Our approach allows leveraging meaningful samples within the database to aid in sampling, thus maximizing the utilization of datasets.
  arXiv  Detail & Related papers  (2024-10-24T13:14:39Z)
• Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting [5.770377200028654]
  We propose a Conditional Denoising Polynomial Modeling (CDPM) framework to model complicated temporal patterns.<n>Instead of modeling the coupled time series, CDPM decomposes it into trend and seasonal components for modeling them separately.<n>For the smooth trend component, a module is proposed to enhance linear models by incorporating historical dependencies.
  arXiv  Detail & Related papers  (2024-10-17T06:20:43Z)
• Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts [103.725112190618]
  This paper introduces Moirai-MoE, using a single input/output projection layer while delegating the modeling of diverse time series patterns to the sparse mixture of experts. Extensive experiments on 39 datasets demonstrate the superiority of Moirai-MoE over existing foundation models in both in-distribution and zero-shot scenarios.
  arXiv  Detail & Related papers  (2024-10-14T13:01:11Z)
• Exploring Representations and Interventions in Time Series Foundation Models [17.224575072056627]
  Time series foundation models (TSFMs) promise to be powerful tools for a wide range of applications.<n>Their internal representations and learned concepts are still not well understood.<n>This study investigates the structure and redundancy of representations across various TSFMs.
  arXiv  Detail & Related papers  (2024-09-19T17:11:27Z)
• Predictive Churn with the Set of Good Models [61.00058053669447]
  This paper explores connections between two seemingly unrelated concepts of predictive inconsistency.<n>The first, known as predictive multiplicity, occurs when models that perform similarly produce conflicting predictions for individual samples.<n>The second concept, predictive churn, examines the differences in individual predictions before and after model updates.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• Non-autoregressive Conditional Diffusion Models for Time Series Prediction [3.9722979176564763]
  TimeDiff is a non-autoregressive diffusion model that achieves high-quality time series prediction. We show that TimeDiff consistently outperforms existing time series diffusion models.
  arXiv  Detail & Related papers  (2023-06-08T08:53:59Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
  We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
  arXiv  Detail & Related papers  (2020-10-27T17:54:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.