Toward Physics-guided Time Series Embedding
Keywords: Time Series Analysis, Dynamic System
Abstract: In various scientific and engineering fields, the primary research areas have revolved around physics-based dynamical systems modeling and data-driven time series analysis. According to the embedding theory, dynamical systems and time series can be mutually transformed using observation functions and physical reconstruction techniques. Based on this, we propose Embedding Duality Theory, where the parameterized embedding layer essentially provides a linear estimation of the non-linear time series dynamics. This theory enables us to bypass the parameterized embedding layer and directly employ physical reconstruction techniques to acquire a data embedding representation. Utilizing physical priors results in a 10$\times$ reduction in parameters, a 3$\times$ increase in speed, and maximum performance enhancements of 18\% in expert, 22\% in zero-shot, and 53\% in few-shot tasks without any hyper-parameter tuning. All methods are encapsulated as a plug-and-play module.
Primary Area: learning on time series and dynamical systems
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Submission Number: 617
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