CAMEO：用于有损时间序列压缩的自相关保持线简化方法
CAMEO: Autocorrelation-Preserving Line Simplification for Lossy Time Series Compression
AI总结：
提出CAMEO，一种保持自相关特性的有损时间序列压缩方法，利用线简化技术，平均压缩比提升2倍，最高54倍，并保持或提升预测精度。
摘要：
来自各种传感器和物联网设备的时间序列数据需要有效的压缩，以减少存储和I/O带宽需求。虽然大多数时间序列数据库和系统依赖于无损压缩，但有损技术以较小的精度损失提供了更大的空间节省。然而，对下游分析应用的未知影响需要半手动试错探索。我们启动了关于有损压缩的工作，该压缩为复杂的统计特征（这些特征与下游分析的准确性密切相关）提供保证。具体来说，我们提出了一种新的有损压缩方法，为时间序列的自相关和偏自相关函数（ACF/PACF）提供保证。我们的方法利用线简化技术以及聚合的增量维护、分块和并行化策略，以实现有效且高效的压缩。结果表明，与以前的有损和无损压缩方法相比，我们的方法在平均压缩比上提高了2倍，在选定数据集上最高提高了54倍。此外，通过保留时间序列的自相关特性，我们保持甚至有时提高预测准确性。我们的框架可扩展到多元时间序列和时间序列的其他统计特征。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
