Understanding the Implicit Biases of Design Choices for Time Series Foundation Models
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912663830790144 |
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| author | Yu, Annan Maddix, Danielle C. Han, Boran Zhang, Xiyuan Ansari, Abdul Fatir Shchur, Oleksandr Faloutsos, Christos Wilson, Andrew Gordon Mahoney, Michael W. Wang, Yuyang |
| author_facet | Yu, Annan Maddix, Danielle C. Han, Boran Zhang, Xiyuan Ansari, Abdul Fatir Shchur, Oleksandr Faloutsos, Christos Wilson, Andrew Gordon Mahoney, Michael W. Wang, Yuyang |
| contents | Time series foundation models (TSFMs) are a class of potentially powerful, general-purpose tools for time series forecasting and related temporal tasks, but their behavior is strongly shaped by subtle inductive biases in their design. Rather than developing a new model and claiming that it is better than existing TSFMs, e.g., by winning on existing well-established benchmarks, our objective is to understand how the various ``knobs'' of the training process affect model quality. Using a mix of theory and controlled empirical evaluation, we identify several design choices (patch size, embedding choice, training objective, etc.) and show how they lead to implicit biases in fundamental model properties (temporal behavior, geometric structure, how aggressively or not the model regresses to the mean, etc.); and we show how these biases can be intuitive or very counterintuitive, depending on properties of the model and data. We also illustrate in a case study on outlier handling how multiple biases can interact in complex ways; and we discuss implications of our results for learning the bitter lesson and building TSFMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19236 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding the Implicit Biases of Design Choices for Time Series Foundation Models Yu, Annan Maddix, Danielle C. Han, Boran Zhang, Xiyuan Ansari, Abdul Fatir Shchur, Oleksandr Faloutsos, Christos Wilson, Andrew Gordon Mahoney, Michael W. Wang, Yuyang Machine Learning Time series foundation models (TSFMs) are a class of potentially powerful, general-purpose tools for time series forecasting and related temporal tasks, but their behavior is strongly shaped by subtle inductive biases in their design. Rather than developing a new model and claiming that it is better than existing TSFMs, e.g., by winning on existing well-established benchmarks, our objective is to understand how the various ``knobs'' of the training process affect model quality. Using a mix of theory and controlled empirical evaluation, we identify several design choices (patch size, embedding choice, training objective, etc.) and show how they lead to implicit biases in fundamental model properties (temporal behavior, geometric structure, how aggressively or not the model regresses to the mean, etc.); and we show how these biases can be intuitive or very counterintuitive, depending on properties of the model and data. We also illustrate in a case study on outlier handling how multiple biases can interact in complex ways; and we discuss implications of our results for learning the bitter lesson and building TSFMs. |
| title | Understanding the Implicit Biases of Design Choices for Time Series Foundation Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.19236 |