Tcm: A High-Precision Lossy Compression Algorithm for Time Series Data With Flexible Queue-Value Dynamic Grouping

Abstract

Piecewise linear approximation (PLA) is pivotal for compressing time-series data under stringent error constraints. However, conventional PLA methods often struggle to reconcile the inherent conflict between aggressive compression ratios (CRs) and the preservation of complex local trends. In this article, we propose Tcm, a high-performance compression framework that achieves a synergistic balance between fidelity and efficiency. Unlike static approximation techniques, Tcm introduces a dynamic error thresholding strategy powered by a trend-aware simulated annealing (SA) optimizer, ensuring a global maximum error bound of < 1%. By integrating multimodal preprocessing—including wavelet denoising, seasonal-trend decomposition (STL)-support vector machine (TSVM) decomposition, and adaptive chunking—Tcm effectively harmonizes fine-grained precision with coarse-grained representation. Experimental evaluations on large-scale industrial datasets demonstrate that Tcm outperforms state-of-the-art benchmarks (e.g., Sim-Piece and Mix-Piece), delivering a 28.1% average improvement in CR and a 40% reduction in segment redundancy. Furthermore, Tcm exhibits superior robustness in high-volatility scenarios, such as financial forecasting and industrial sensing. By optimizing the “computation-for-transmission” tradeoff, Tcm provides a scalable and energy-efficient solution for real-time telemetry and edge computing applications.