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Koopman Operator Framework for Time Series Modeling and Analysis

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Abstract

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

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Acknowledgements

Funding provided by United Technologies Research Center is greatly appreciated. The author would like to thank Matthew Williams, Milos Ilak, and Andrzej Banaszuk for careful reading of the manuscript and valuable feedback.

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    Amit Surana

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Communicated by Clancy Rowley and Ioannis Kevrekidis.

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Surana, A. Koopman Operator Framework for Time Series Modeling and Analysis. J Nonlinear Sci 30, 1973–2006 (2020). https://doi.org/10.1007/s00332-017-9441-y

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