  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
