OneShotSTL：用于在线时间序列异常检测与预测的一次性季节-趋势分解
OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting
AI总结：
提出OneShotSTL算法，通过O(1)复杂度的在线季节-趋势分解，解决实时时间序列分析延迟问题，速度提升超千倍且保持高精度。
摘要：
季节-趋势分解是时间序列分析中最基础的概念之一，支持包括时间序列异常检测和预测在内的各种下游任务。然而，现有的分解方法依赖于批处理，其时间复杂度为O(W)，其中W是一个时间窗口内的数据点数量。因此，它们不能始终高效地支持要求低处理延迟的实时分析。为了应对这一挑战，我们提出了OneShotSTL，这是一种高效且准确的算法，能够以O(1)的更新时间复杂度在线分解时间序列。OneShotSTL比批处理方法快1,000倍以上，且准确性与最佳同类方法相当。在真实世界基准数据集上针对下游时间序列异常检测和预测任务的大量实验表明，OneShotSTL比现有最先进的方法快10到1,000倍以上，同时仍提供相当甚至更好的准确性。
英文摘要：
Seasonal-trend decomposition is one of the most fundamental concepts in time series analysis that supports various downstream tasks, including time series anomaly detection and forecasting. However, existing decomposition methods rely on batch processing with a time complexity of O(W), where W is the number of data points within a time window. Therefore, they cannot always efficiently support real-time analysis that demands low processing delay. To address this challenge, we propose OneShotSTL, an efficient and accurate algorithm that can decompose time series online with an update time complexity of O(1). OneShotSTL is more than $1,000$ times faster than the batch methods, with accuracy comparable to the best counterparts. Extensive experiments on real-world benchmark datasets for downstream time series anomaly detection and forecasting tasks demonstrate that OneShotSTL is from 10 to over 1,000 times faster than the state-of-the-art methods, while still providing comparable or even better accuracy.
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。
