Fast Automatic Feature Selection for Multi-Period Sliding Window Aggregate in Time Series

TL;DR

This paper introduces a fast, automatic feature selection framework for multi-period sliding window aggregates in time series, leveraging Markov Chain theory to improve efficiency and accuracy over traditional methods.

Contribution

It presents a novel Markov Chain-based framework that automates and accelerates feature selection for sliding window aggregates in time series analysis.

Findings

High accuracy in feature selection demonstrated

Significant reduction in computation time achieved

Framework easily extendable to various window types and operators

Abstract

As one of the most well-known artificial feature sampler, the sliding window is widely used in scenarios where spatial and temporal information exists, such as computer vision, natural language process, data stream, and time series. Among which time series is common in many scenarios like credit card payment, user behavior, and sensors. General feature selection for features extracted by sliding window aggregate calls for time-consuming iteration to generate features, and then traditional feature selection methods are employed to rank them. The decision of key parameter, i.e. the period of sliding windows, depends on the domain knowledge and calls for trivial. Currently, there is no automatic method to handle the sliding window aggregate features selection. As the time consumption of feature generation with different periods and sliding windows is huge, it is very hard to enumerate them all and then select them. In this paper, we propose a general framework using Markov Chain to solve this problem. This framework is very efficient and has high accuracy, such that it is able to perform feature selection on a variety of features and period options. We show the detail by 2 common sliding windows and 3 types of aggregation operators. And it is easy to extend more sliding windows and aggregation operators in this framework by employing existing theory about Markov Chain.