RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
This addresses the need for robust forecasting models in real-world applications where data anomalies are common, representing an incremental improvement over prior methods.
The paper tackles the problem of time series forecasting with contaminated training data by statistically defining three anomaly types and analyzing loss and sample robustness, proposing a simple algorithm that outperforms existing approaches in experiments.
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.