Fast Automatic Feature Selection for Multi-Period Sliding Window Aggregate in Time Series
This work provides an automatic and efficient feature selection method for time series analysis, which is beneficial for data scientists and machine learning practitioners working with sliding window features.
The paper addresses the problem of automatically selecting features from multi-period sliding window aggregates in time series data, which traditionally requires time-consuming iteration and manual parameter tuning. The proposed Markov Chain-based framework efficiently performs feature selection across various features and period options with high accuracy.
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.