Lossless Compression of Time Series Data: A Comparative Study
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915539472875520 |
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| author | Matt, Jonas G. Huang, Pengcheng Maag, Balz |
| author_facet | Matt, Jonas G. Huang, Pengcheng Maag, Balz |
| contents | Our increasingly digital and connected world has led to the generation of unprecedented amounts of data. This data must be efficiently managed, transmitted, and stored to preserve resources and allow scalability. Data compression has therein been a key technology for a long time, resulting in a vast landscape of available techniques. This largest-to-date study analyzes and compares various lossless data compression methods for time series data. We present a unified framework encompassing two stages: data transformation and entropy encoding. We evaluate compression algorithms across both synthetic and real-world datasets with varying characteristics. Through ablation studies at each compression stage, we isolate the impact of individual components on overall compression performance -- revealing the strengths and weaknesses of different algorithms when facing diverse time series properties. Our study underscores the importance of well-configured and complete compression pipelines beyond individual components or algorithms; it offers a comprehensive guide for selecting and composing the most appropriate compression algorithms tailored to specific datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lossless Compression of Time Series Data: A Comparative Study Matt, Jonas G. Huang, Pengcheng Maag, Balz Information Theory Our increasingly digital and connected world has led to the generation of unprecedented amounts of data. This data must be efficiently managed, transmitted, and stored to preserve resources and allow scalability. Data compression has therein been a key technology for a long time, resulting in a vast landscape of available techniques. This largest-to-date study analyzes and compares various lossless data compression methods for time series data. We present a unified framework encompassing two stages: data transformation and entropy encoding. We evaluate compression algorithms across both synthetic and real-world datasets with varying characteristics. Through ablation studies at each compression stage, we isolate the impact of individual components on overall compression performance -- revealing the strengths and weaknesses of different algorithms when facing diverse time series properties. Our study underscores the importance of well-configured and complete compression pipelines beyond individual components or algorithms; it offers a comprehensive guide for selecting and composing the most appropriate compression algorithms tailored to specific datasets. |
| title | Lossless Compression of Time Series Data: A Comparative Study |
| topic | Information Theory |
| url | https://arxiv.org/abs/2510.07015 |