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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 29009-29029 |
| Number of pages | 21 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 202 |
| State | Published - 2023 |
| Event | 40th International Conference on Machine Learning, ICML 2023 - Honolulu, United States Duration: Jul 23 2023 → Jul 29 2023 |
Funding
JH would like to thank to Donglin Yang and Andrew Chen for enlightening discussions, and Jiayi Wang for invaluable support in facilitating experimental deployments. The authors would like to thank the anonymous reviewers and program chairs for constructive comments. JH is partially supported by the Walter P. Murphy Fellowship. HL is partially supported by NIH R01LM1372201, NSF CAREER1841569 and a NSF TRIPODS1740735. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability