Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918312434204672 |
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| author | Liu, Ziqi Zeng, Pei Ding, Yi |
| author_facet | Liu, Ziqi Zeng, Pei Ding, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00614 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality Liu, Ziqi Zeng, Pei Ding, Yi Machine Learning Artificial Intelligence 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. |
| title | Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.00614 |