Time series compression based on reinforcement learning

Abstract

Nowadays, sensors and signal catchers in various fields are capturing time-series data all the time, and time-series data are exploding. Due to the large storage space requirements and redundancy, many compression techniques for time series have been proposed. However, the existing compression algorithms still face the challenge of the contradiction between random access and compression ratio. That is, in a time series database, large-scale time series data have high requirements on compression ratio, while large pieces of data need to be decompressed during the access process, resulting in poor query efficiency. In this paper, a proper solution is proposed to resolve such a contradiction. We propose a data compression method based on reinforcement learning, and use the idea of data deduplication to design the data compression method, so that the queries can be processed without decompression. We theoretically show that the proposed approach is effective and could ensure random accessing. To efficiently implement the reinforcement-learning-based solution, we develop a data compression method based on DQN network. Experiments show that the proposed algorithm performs well in time series data sets with large amount of data and strong regularity, performs well in compression ratio and compression time. Besides, since no decompression is required, the query processing time is much less than the competitors.