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.

翻译：我们引入了用于时间序列建模的可编程特征工程概念，并提出了一种特征编程框架。该框架能够为含噪多元时间序列生成大量预测特征，同时允许用户以最小代价融入其归纳偏置。这一框架的核心动机在于：将任意多元时间序列视为细粒度轨迹增量的累积和，其中每个增量由一种新型的自旋-气体动力学伊辛模型所支配。这种细粒度视角促使我们开发出一组简洁的算子集合，能以抽象方式概括多元时间序列，从而为大规模自动化特征工程奠定基础。在数值验证方面，我们在多个合成和真实世界的含噪时间序列数据集上验证了本方法的有效性。