Adaptive Lossless Segmented Compression Method Integrating Temporal Dependencies and Data Features

Compression algorithms struggle to maintain a high compression ratio when handling complex and diverse patterns in time series data. Thus, selecting the appropriate compression algorithms tailored to different patterns is an urgent requirement. Existing adaptive compression schemes have low accuracy when determining the optimal compression algorithm. To address this issue, this paper proposes an Adaptive Lossless Segmented Compression method integrating Temporal Dependencies and data Features (ALSC-TDF). This method performs segmented compression of time series data and selects the most suitable compression algorithm based on the pattern of each segment. ALSC-TDF converts the compression algorithm selection problem into a time series classification task; utilizes Gated Recurrent Unit (GRU) to capture temporal dependencies; and considers compression efficiency features that are closely related to the data compression ratio, including basic statistical features, permutation and variation features, and compression degree features. Temporal dependencies and proposed features are analyzed using a modified GRU-Fully Convolutional Network (GRU-FCN) to improve classification accuracy and robustness, thereby improving the overall data compression ratio. The effectiveness and advantages of ALSC-TDF are verified using multiple datasets, and it outperforms comparison models in terms of classification accuracy and F1 value, with an accuracy of 88.86%. Moreover, ALSC-TDF achieves a significantly better compression ratio than existing compression algorithms, with a 15.62% improvement in overall data compression ratio compared to that of the Elf algorithm. Experimental results indicate that comprehensively analyzing the data features and temporal dependencies of time series can greatly improve the accuracy and robustness of adaptive compression algorithm selection, thereby achieving a higher compression ratio.