LFZip: Lossy compression of multivariate floating-point time series data via improved prediction
This work addresses compression needs for IoT and sensor data, offering improved efficiency for downstream applications, though it is incremental as it builds on the prediction-quantization-entropy coder framework with enhancements.
The authors tackled the problem of compressing noisy multivariate floating-point time series data by proposing LFZip, an error-bounded lossy compressor that guarantees reconstruction within user-specified error bounds, outperforming existing state-of-the-art compressors on several datasets.
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