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.

翻译：季节趋势分解是时间序列分析中最基础的概念之一，支撑着包括时间序列异常检测和预测在内的多种下游任务。然而，现有分解方法依赖批处理方式，其时间复杂度为O(W)，其中W为时间窗口内的数据点数量，因此无法高效支持对低延迟要求的实时分析。针对这一挑战，我们提出OneShotSTL，一种高效且精确的算法，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1000倍以上，同时精度与最优方法相当。在面向下游时间序列异常检测和预测任务的真实世界基准数据集上的大量实验表明，OneShotSTL比最先进方法快10倍到1000倍以上，同时仍能提供相当甚至更优的精度。