Implicit Reasoning in Deep Time Series Forecasting
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916475398258688 |
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| author | Potosnak, Willa Challu, Cristian Goswami, Mononito Wiliński, Michał Żukowska, Nina Dubrawski, Artur |
| author_facet | Potosnak, Willa Challu, Cristian Goswami, Mononito Wiliński, Michał Żukowska, Nina Dubrawski, Artur |
| contents | Recently, time series foundation models have shown promising zero-shot forecasting performance on time series from a wide range of domains. However, it remains unclear whether their success stems from a true understanding of temporal dynamics or simply from memorizing the training data. While implicit reasoning in language models has been studied, similar evaluations for time series models have been largely unexplored. This work takes an initial step toward assessing the reasoning abilities of deep time series forecasting models. We find that certain linear, MLP-based, and patch-based Transformer models generalize effectively in systematically orchestrated out-of-distribution scenarios, suggesting underexplored reasoning capabilities beyond simple pattern memorization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10840 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Implicit Reasoning in Deep Time Series Forecasting Potosnak, Willa Challu, Cristian Goswami, Mononito Wiliński, Michał Żukowska, Nina Dubrawski, Artur Machine Learning Recently, time series foundation models have shown promising zero-shot forecasting performance on time series from a wide range of domains. However, it remains unclear whether their success stems from a true understanding of temporal dynamics or simply from memorizing the training data. While implicit reasoning in language models has been studied, similar evaluations for time series models have been largely unexplored. This work takes an initial step toward assessing the reasoning abilities of deep time series forecasting models. We find that certain linear, MLP-based, and patch-based Transformer models generalize effectively in systematically orchestrated out-of-distribution scenarios, suggesting underexplored reasoning capabilities beyond simple pattern memorization. |
| title | Implicit Reasoning in Deep Time Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.10840 |