Learned Compression of Nonlinear Time Series With Random Access

TL;DR

NeaTS is a novel compression scheme for nonlinear time series that offers high compression ratios, fast random access, and error guarantees, enabling efficient storage and real-time analysis of large-scale data.

Contribution

The paper introduces NeaTS, a new random-accessible compression method that models time series with nonlinear functions and bounds residuals for lossless or lossy compression.

Findings

NeaTS improves compression ratios by up to 14% over state-of-the-art lossy methods.

It achieves faster decompression speeds compared to existing approaches.

NeaTS provides highly efficient random access for massive time series data.

Abstract

Time series play a crucial role in many fields, including finance, healthcare, industry, and environmental monitoring. The storage and retrieval of time series can be challenging due to their unstoppable growth. In fact, these applications often sacrifice precious historical data to make room for new data. General-purpose compressors can mitigate this problem with their good compression ratios, but they lack efficient random access on compressed data, thus preventing real-time analyses. Ad-hoc streaming solutions, instead, typically optimise only for compression and decompression speed, while giving up compression effectiveness and random access functionality. Furthermore, all these methods lack awareness of certain special regularities of time series, whose trends over time can often be described by some linear and nonlinear functions. To address these issues, we introduce NeaTS, a randomly-accessible compression scheme that approximates the time series with a sequence of nonlinear functions of different kinds and shapes, carefully selected and placed by a partitioning algorithm to minimise the space. The approximation residuals are bounded, which allows storing them in little space and thus recovering the original data losslessly, or simply discarding them to obtain a lossy time series representation with maximum error guarantees. Our experiments show that NeaTS improves the compression ratio of the state-of-the-art lossy compressors that use linear or nonlinear functions (or both) by up to 14%. Compared to lossless compressors, NeaTS emerges as the only approach to date providing, simultaneously, compression ratios close to or better than the best existing compressors, a much faster decompression speed, and orders of magnitude more efficient random access, thus enabling the storage and real-time analysis of massive and ever-growing amounts of (historical) time series data.