Feature Programming for Multivariate Time Series Prediction

• URL: http://arxiv.org/abs/2306.06252v1
• Date: Fri, 9 Jun 2023 20:46:55 GMT
• Title: Feature Programming for Multivariate Time Series Prediction
• Authors: Alex Reneau, Jerry Yao-Chieh Hu, Chenwei Xu, Weijian Li, Ammar Gilani, Han Liu
• Abstract summary: We introduce the concept of programmable feature engineering for time series modeling. We propose a feature programming framework that generates large amounts of predictive features for noisy time series.
• Score: 7.0220697993232
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce the concept of programmable feature engineering for time series modeling and propose a feature programming framework. This framework generates large amounts of predictive features for noisy multivariate time series while allowing users to incorporate their inductive bias with minimal effort. The key motivation of our framework is to view any multivariate time series as a cumulative sum of fine-grained trajectory increments, with each increment governed by a novel spin-gas dynamical Ising model. This fine-grained perspective motivates the development of a parsimonious set of operators that summarize multivariate time series in an abstract fashion, serving as the foundation for large-scale automated feature engineering. Numerically, we validate the efficacy of our method on several synthetic and real-world noisy time series datasets.

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