The explosive growth of time series gives rise to a large amount of data, which emphasizes the importance of data compression. The data compression not only reduces storage costs but also enhances data transmission efficiency and processing speed. However, traditional compression algorithms usually suffer an insufficient compression ratio and an excessive computational cost. To address these problems above, in this paper, we propose a two-stage compression algorithm for the large-scale time series data. In the first stage, we transform the time series data into low-volatility residual data by using Autoregressive Integrated Moving Average (ARIMA) modeling and apply adaptive precision quantization to improve compressibility. In the second stage, we implement a reinforcement learning-based compression strategy, which utilizes the Q-learning to select the number of blocks to divide the quantized data segment and achieves compression by storing the same content between the divided data blocks only once and storing the different content separately; and we incorporate the Upper Confidence Bound (UCB) to balance exploration and exploitation in order to track changes in data patterns and improve compression performance. Experimental results demonstrate that our algorithm achieves a higher compression ratio while maintaining a low computational complexity compared with traditional compression algorithms.
Chi et al. (Sun,) studied this question.