Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data
This addresses efficiency challenges for real-world multichannel time series prediction applications, though it appears to be an incremental improvement building on MIMO methods.
The paper tackles the problem of compressing multichannel time series data for efficient prediction in edge and cloud environments, proposing a predictability-aware compression-decompression framework that reduces runtime and communication costs while maintaining prediction accuracy across six datasets and various predictors.
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 success of Multiple-Input Multiple-Output (MIMO) methods, we propose a predictability-aware compression-decompression framework to reduce runtime, lower communication cost, and maintain prediction accuracy across diverse predictors. The core idea involves using a circular periodicity key matrix with orthogonality to capture underlying time series predictability during compression and to mitigate reconstruction errors during decompression by relaxing oversimplified data assumptions. Theoretical and empirical analyses show that the proposed framework is both time-efficient and scalable 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.