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

arXiv: https://arxiv.org/abs/2304.01506 · HTML: https://mlsanity.com/paper/2304.01506/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting](https://mlsanity.com/paper/2205.08897/) (2022)
- [Towards a Rigorous Evaluation of Time-series Anomaly Detection](https://mlsanity.com/paper/2109.05257/) (2021)
- [RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series](https://mlsanity.com/paper/1812.01767/) (2018)
- [Efficient and Scalable Multi-task Regression on Massive Number of Tasks](https://mlsanity.com/paper/1811.05695/) (2018)
- [OnlineSTL: Scaling Time Series Decomposition by 100x](https://mlsanity.com/paper/2107.09110/) (2021)

## Built on by (most cited first, 10 of 10)

- [SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters](https://mlsanity.com/paper/2405.00946/) (2024)
- [ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning](https://mlsanity.com/paper/2412.03104/) (2024)
- [Automatic Database Configuration Debugging using Retrieval-Augmented Language Models](https://mlsanity.com/paper/2412.07548/) (2024)
- [Conformal time series decomposition with component-wise exchangeability](https://mlsanity.com/paper/2406.16766/) (2024)
- [Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]](https://mlsanity.com/paper/2308.12899/) (2023)
- [Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services](https://mlsanity.com/paper/2507.12908/) (2025)
- [Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering](https://mlsanity.com/paper/2505.05738/) (2025)
- [Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scaling](https://mlsanity.com/paper/2508.12773/) (2025)
- [Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks](https://mlsanity.com/paper/2605.00482/) (2026)
- [Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environments](https://mlsanity.com/paper/2607.23503/) (2026)

## Abstract (arXiv)

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.