RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies

TL;DR

This paper introduces RobustTSF, a method for robust time series forecasting that effectively handles anomalies in training data, supported by theoretical analysis and extensive experiments showing superior performance.

Contribution

The paper defines anomaly types, analyzes robustness theoretically and experimentally, and proposes a simple algorithm for robust forecasting from contaminated data.

Findings

RobustTSF outperforms existing methods in robustness.

Theoretical analysis confirms loss and sample robustness.

Extensive experiments validate effectiveness on contaminated data.

Abstract

Time series forecasting is an important and forefront task in many real-world applications. However, most of time series forecasting techniques assume that the training data is clean without anomalies. This assumption is unrealistic since the collected time series data can be contaminated in practice. The forecasting model will be inferior if it is directly trained by time series with anomalies. Thus it is essential to develop methods to automatically learn a robust forecasting model from the contaminated data. In this paper, we first statistically define three types of anomalies, then theoretically and experimentally analyze the loss robustness and sample robustness when these anomalies exist. Based on our analyses, we propose a simple and efficient algorithm to learn a robust forecasting model. Extensive experiments show that our method is highly robust and outperforms all existing approaches. The code is available at https://github.com/haochenglouis/RobustTSF.