提出一种保留自相关性的时间序列压缩方法，提升存储效率同时保障分析精度。
CAMEO: Autocorrelation-Preserving Line Simplification for Lossy Time Series Compression
- 基于线段简化与增量聚合，确保压缩后自相关性不变。
- 平均压缩比提升2倍，最高达54倍，且预测准确率不降反升。
- 适合对时序统计特性敏感的物联网与数据分析场景。
来自各类传感器和物联网设备的时间序列数据需要高效压缩以减少存储和输入输出带宽。尽管多数时间序列数据库系统依赖无损压缩，有损技术可在微小精度损失下实现更大空间节省。然而，其对下游分析应用的影响未知，常需半人工试错。本文首次研究提供复杂统计特征保障的有损压缩方法，特别针对自相关函数（ACF）和偏自相关函数（PACF）进行保证。所提方法结合线段简化、增量聚合、分块与并行化策略，实现高效压缩。实验表明，相比先前有损与无损压缩方法，本方法平均压缩比提升2倍，部分数据集高达54倍；同时保持甚至提升了预测精度，因有效保留了时间序列的自相关特性。该框架可扩展至多变量时间序列及其他统计特征。
原文摘要 · Abstract (English)
Time series data from a variety of sensors and IoT devices need effective compression to reduce storage and I/O bandwidth requirements. While most time series databases and systems rely on lossless compression, lossy techniques offer even greater space-saving with a small loss in precision. However, the unknown impact on downstream analytics applications requires a semi-manual trial-and-error exploration. We initiate work on lossy compression that provides guarantees on complex statistical features (which are strongly correlated with the accuracy of the downstream analytics). Specifically, we propose a new lossy compression method that provides guarantees on the autocorrelation and partial-autocorrelation functions (ACF/PACF) of a time series. Our method leverages line simplification techniques as well as incremental maintenance of aggregates, blocking, and parallelization strategies for effective and efficient compression. The results show that our method improves compression ratios by 2x on average and up to 54x on selected datasets, compared to previous lossy and lossless compression methods. Moreover, we maintain -- and sometimes even improve -- the forecasting accuracy by preserving the autocorrelation properties of the time series. Our framework is extensible to multivariate time series and other statistical features of the time series.
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