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LFZip: Lossy compression of multivariate floating-point time series data via improved prediction

LFZip: Lossy compression of multivariate floating-point time series data via improved prediction

来源:Arxiv_logoArxiv
英文摘要

Time series data compression is emerging as an important problem with the growth in IoT devices and sensors. Due to the presence of noise in these datasets, lossy compression can often provide significant compression gains without impacting the performance of downstream applications. In this work, we propose an error-bounded lossy compressor, LFZip, for multivariate floating-point time series data that provides guaranteed reconstruction up to user-specified maximum absolute error. The compressor is based on the prediction-quantization-entropy coder framework and benefits from improved prediction using linear models and neural networks. We evaluate the compressor on several time series datasets where it outperforms the existing state-of-the-art error-bounded lossy compressors. The code and data are available at https://github.com/shubhamchandak94/LFZip

Tsachy Weissman、Kedar Tatwawadi、Juan Aparicio、Chengtao Wen、Lingyun Wang、Shubham Chandak

计算技术、计算机技术

Tsachy Weissman,Kedar Tatwawadi,Juan Aparicio,Chengtao Wen,Lingyun Wang,Shubham Chandak.LFZip: Lossy compression of multivariate floating-point time series data via improved prediction[EB/OL].(2019-11-01)[2025-05-17].https://arxiv.org/abs/1911.00208.点此复制

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