Are there lossless compression methods tailored for time-series data?

NNCP is an experiment to build a practical lossless data compressor with neural networks. The latest version uses a Transformer model. The papers nncp_v2.1.pdf ... This paper focuses on the Adaptive Differential Pulse Code Modulation (ADPCM) that encodes a data stream into a constant bit length per data element. Nov 20, 2018 ... We combine recurrent neural network predictors with an arithmetic coder and losslessly compress a variety of synthetic, text and genomic datasets. Description. Data compression using neural networks. DeepZip: Lossless Data Compression using Recurrent Neural Networks. Requirements. GPU, nvidia-docker (or ... This data compression pipeline is using algorithms like Burrows-Wheeler transform (BWT) and Move to front (MTF) to improve the Huffman compression. Sep 25, 2019 ... In this paper, we propose a Transformer based lossless compression method that match the best compression ratio for text. Our approach is purely ... New LZ4 & Snappy IP Core from CAST Enables Fast Lossless Data Decompression. This first LZ4/Snappy IP core ASIC or FPGA hardware engine offloads a system's CPU ... 1. General-purpose lossless compression. The old compressors: zip/gzip, bzip2, lzma are still widely used. We have new compressors: lz4, zstd and brotli but ... Lossless compression algorithms allow you to reduce your file size without sacrificing any of the original data in the process. You're generally able to restore ... Arithmetic encoding (AE) is a lossless algorithm that uses a low number of bits to compress data. It's an entropy-based algorithm.