Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality

TL;DR

This paper introduces a predictability-aware compression framework for multichannel time series data that reduces runtime and communication costs while preserving prediction accuracy, leveraging seasonal patterns and orthogonal key matrices.

Contribution

It proposes a novel compression-decompression method using seasonal key matrices that improves efficiency and accuracy across diverse predictors in multichannel time series.

Findings

Achieves superior performance in prediction accuracy and runtime.

Maintains compatibility with various predictors.

Theoretically proven to be time-efficient and accuracy-preserving.

Abstract

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.