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

TL;DR

LFZip is a novel lossy compression method for multivariate floating-point time series data that guarantees reconstruction within a user-defined error margin, leveraging advanced prediction techniques to outperform existing methods.

Contribution

It introduces LFZip, an error-bounded lossy compressor using improved prediction models for better compression of noisy multivariate time series data.

Findings

Outperforms existing error-bounded lossy compressors on multiple datasets

Provides guaranteed reconstruction within specified error bounds

Utilizes linear and neural network-based prediction for improved compression

Abstract

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