OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting

Abstract

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.

PVLDB 2023

References in corpus (6)

• FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
• TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
• FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
• RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection
• Towards a Rigorous Evaluation of Time-series Anomaly Detection
• OnlineSTL: Scaling Time Series Decomposition by 100x

Cited by in corpus (1)

• ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning