RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

TL;DR

RobustSTL introduces a new decomposition method for long time series that effectively handles seasonality shifts, anomalies, and abrupt trend changes, improving accuracy in real-world applications.

Contribution

It presents a robust, generic algorithm combining least absolute deviations regression and non-local seasonal filtering for improved time series decomposition.

Findings

Outperforms existing methods on synthetic datasets.

Effective in handling seasonality shifts and anomalies.

Demonstrates superior accuracy on real-world data.

Abstract

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.