计算机工程2026，Vol.52Issue(2)：79-88,10.DOI:10.19678/j.issn.1000-3428.0069787
融合时序依赖性与数据特征的自适应无损分段压缩方法
Adaptive Lossless Segmented Compression Method Integrating Temporal Dependencies and Data Features
摘要
Abstract
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 Fl 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.关键词
时序数据/自适应压缩/模式识别/门控循环单元/特征提取Key words
time series data/adaptive compression/pattern recognition/Gated Recurrent Unit(GRU)/feature extraction分类
信息技术与安全科学引用本文复制引用
陈振清,万加富,张锐..融合时序依赖性与数据特征的自适应无损分段压缩方法[J].计算机工程,2026,52(2):79-88,10.基金项目
国家自然科学基金(U1801264). （U1801264）
