Twin learning for domain agnostic time series analysis: A regime-switch approach

Correlations among variables in complex ecosystems such as weather systems and financial markets result in large amounts of dynamic and co-evolving time sequences. The benefits of discovering and predicting intricate patterns (aka regimes) in time sequences are multifold, including better understanding of the ecosystem dynamics, optimizing model selection, and improving interpretability of results. Despite recent advancements, existing methods primarily emphasize predictive accuracy, which might overshadow the need to comprehend the structural dynamics within the series. Additionally, these methods often encounter one or more of the following limitations: (1) difficulty in identifying regimes within domain-dependent segmentations; (2) inability to integrate nonlinear relationships across time series; (3) lack of an effective method to encapsulate the temporal behaviors. To tackle these challenges, we introduce a twin learning regime-switch model to simultaneously learn domain-agnostic segmentation and regime switch in a principled way. Specifically, we devise a kernel-based method that determines the duration of regime and captures dynamic switches through potent representations, accounting for the non-linear interactions between series. With this model, it is feasible to automatically achieve the two subtasks of identifying the optimal regimes and determining the most suitable segmentation. Experimental results on synthetic and real-world datasets indicate that our method is capable of revealing the structures that underpin the behavior of co-evolving ecosystems, which display different dynamics. These structures can be leveraged to better define regimes with superior predictive capabilities compared to widely used traditional models and state-of-the-art neural network models. • We propose a novel regime-switch model to identify domain-agnostic regimes in time series. • We develop a kernel representation learning approach to capturing dynamic regime switches. • This representation allows effectively modeling nonlinear interactions and co-evolving patterns in time series. • Our model transforms heavy sets of time series into a lighter and meaningful structure, enabling a deeper understanding of structural dynamics. • The extensive experimental results showcase superior predictive performance compared to traditional and state-of-the-art methods.

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