Feature Programming for Multivariate Time Series Prediction
Alex Reneau Jerry Yao-Chieh Hu Chenwei Xu Weijian Li Ammar Gilani Han Liu
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.