针对多通道时间序列，提出可预测性感知的压缩解压框架，兼顾效率与精度。
Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality
- 用正交循环季节键矩阵捕捉时间序列可预测性，实现高效压缩。
- 在六组数据上验证，显著降低运行时间且保持预测精度。
- 适合边缘与云端部署，兼容多种预测模型，适合工业级应用。
现实世界中的多通道时间序列预测对边缘与云端环境的效率要求日益提高，通道压缩成为迫切且关键的问题。受信号处理中多输入多输出（MIMO）方法成功的启发，我们提出一种可预测性感知的压缩-解压框架，旨在降低运行时间、减少通信开销，并在多种预测器上保持预测精度。核心思想是利用具有正交性的循环季节键矩阵，在压缩阶段捕捉底层时间序列的可预测性，并在解压阶段通过引入更真实的数据假设来缓解重建误差。理论分析表明，该框架在通道数量较多时兼具时间效率与精度保持性。在六个不同数据集上的广泛实验显示，该方法通过联合优化预测精度与运行时间，实现了整体性能的优越表现，同时对多种预测器具有强兼容性。
原文摘要 · Abstract (English)
Real-world multichannel time series prediction faces growing demands for efficiency across edge and cloud environments, making channel compression a timely and essential problem. Motivated by the success of Multiple-Input Multiple-Output (MIMO) methods in signal processing, we propose a predictability-aware compression-decompression framework to reduce runtime, decrease communication cost, and maintain prediction accuracy across diverse predictors. The core idea involves using a circular seasonal key matrix with orthogonality to capture underlying time series predictability during compression and to mitigate reconstruction errors during decompression by introducing more realistic data assumptions. Theoretical analyses show that the proposed framework is both time-efficient and accuracy-preserving under a large number of channels. Extensive experiments on six datasets across various predictors demonstrate that the proposed method achieves superior overall performance by jointly considering prediction accuracy and runtime, while maintaining strong compatibility with diverse predictors.
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