Rabbit: Adaptive Lossless Compression for Floating-Point Time Series via Temporal Locality-Aware Dynamic Encoding

With the advancement of IoT technology, a vast amount of floating-point time series data has emerged, posing significant challenges for data storage and transmission. To address this issue, the efficient compression of floating-point time series data has become increasingly important. In the field of lossless compression where precision loss is not allowed, compression and decompression are a symmetrical and reversible transformation. The optimization of its encoding and decoding strategies remains the current optimal path for lossless compression. Based on the existing lossless compression algorithms for time series, this paper proposes Rabbit, which is a new floating-point time series data stream lossless compression algorithm. This method can perceive the data characteristics and, by leveraging the temporal locality of the time series, predict the branch distribution of the data stream during compression, thereby dynamically encoding the flag bits. This algorithm designs a TOE encoding method specifically for the significant bits to reduce the number of compressed bits. Compared with traditional floating-point compression schemes, its performance has been significantly improved. Experimental evaluations on 28 datasets show that this algorithm consistently outperforms existing methods with an average improvement of 4.15% over the baseline ACTF algorithm. Notably, on datasets such as server31, server34, and server41, the compression ratio can be reduced by up to 43.04%. Additionally, the compression and decompression time metrics have improved by 4.27% and 3.74%, respectively. Overall, Rabbit offers an effective lossless compression approach for floating-point time-series data, improving the compression ratio without compromising encoding/decoding throughput.