TimesJev: Thinking Fast on Time Series with System 1 Sufficiency
Large language models (LLMs) have recently shown strong potential for time series reasoning, while models that reason over time series are usually with long chains of thought.
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Key points
- In contrast, System 1 models such as Jev make decisions instantly and can naturally be an alternative, but raw time series are not compatible with the embedding space of these models, thus hindering the reasoning performance, especially when input series contain thousands of numeric points.
- In this paper, we make a very early attempt to propose a time series System 1 model, namely TimesJev.
- Specifically, we first formalize System 1 sufficiency in time series reasoning, which motivates a compressed representation for time series with key frequency patterns preserved.
- We optimize this objective by learning time series compression with a gating mechanism for sufficiency, and then aligning the selected representation with the embedding space for realizability.
Sources (1)
- [1]TimesJev: Thinking Fast on Time Series with System 1 SufficiencyarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 08:26 AM
Large language models (LLMs) have recently shown strong potential for time series reasoning, while models that reason over time series are usually with long chains of thought.
In contrast, System 1 models such as Jev make decisions instantly and can naturally be an alternative, but raw time series are not compatible with the embedding space of these models, thus hindering the reasoning performance, especially when input series contain thousands of numeric points.
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