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

Ziqi Liu, Pei Zeng, Yi Ding

TL;DR

This work tackles efficient multichannel time series forecasting by introducing a predictability-aware compression–decompression framework that preserves latent temporal dependencies while dramatically reducing runtime and communication costs. The method leverages seasonal circular-key encoding, orthogonal keys, and a residual-based decompression block to maintain predictive information under a single-channel compressed representation, with theoretical guarantees grounded in information bottleneck analysis. Empirical results across six edge datasets and multiple backbones demonstrate improved CDPI, lower MSE, and faster training/inference, validating broad applicability and scalability. The approach offers a practical, flexible solution for edge–cloud forecasting, particularly in resource-constrained environments, though it requires domain-specific training and may face stability challenges at very high compression ratios.

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.

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

TL;DR

This work tackles efficient multichannel time series forecasting by introducing a predictability-aware compression–decompression framework that preserves latent temporal dependencies while dramatically reducing runtime and communication costs. The method leverages seasonal circular-key encoding, orthogonal keys, and a residual-based decompression block to maintain predictive information under a single-channel compressed representation, with theoretical guarantees grounded in information bottleneck analysis. Empirical results across six edge datasets and multiple backbones demonstrate improved CDPI, lower MSE, and faster training/inference, validating broad applicability and scalability. The approach offers a practical, flexible solution for edge–cloud forecasting, particularly in resource-constrained environments, though it requires domain-specific training and may face stability challenges at very high compression ratios.

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.

Paper Structure

This paper contains 56 sections, 32 equations, 4 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Time Series Prediction
  4. Time Series Data Compression
  5. Multiple-Input Multiple-Output (MIMO)
  6. Problem Definition
  7. Definition 1:
  8. Assumption 1:
  9. Objective Function
  10. Observations and Theoretical Foundations
  11. Redundancy in Multichannel Time Series
  12. Multi-channel time series data can be represented by a limited number of channels.
  13. Multi-channel time series data exists redundancy about similar pattern and low variation components.
  14. Retaining Predictive Information
  15. Assumption 2: Gaussian redundancy assumption in multichannel time series.
  16. ...and 41 more sections

Figures (4)

  • Figure 1: Time series data compression in two example situations.
  • Figure 2: Heatmaps of raw multichannel time series data. The vertical axis represents 321 channels, and the horizontal axis denotes 321 time steps. Highlighted regions indicate examples of similar patterns.
  • Figure 3: Overall architecture of the compression–decompression framework. Compression and decompression processes take place at the sensor or edge, while prediction is performed in the cloud. The framework is compatible with different deep learning prediction modules, such as transformer-based, MLP-based, and linear-based modules.
  • Figure 4: Boxplot of MSE and runtime across channel configurations and datasets