Causal-TSF: A Causal Intervention Approach to Mitigate Confounding Bias in Time Series Forecasting

Qinkang Gong, Yan Pan, Hanjiang Lai, Rongbang Qiu, Jian Yin

Abstract: Time series forecasting, aiming to learn models from historical data and predict future values in time series, is a fundamental research topic in machine learning. However, few efforts have been devoted to addressing the confounding effects in time series data, e.g., the historical data are affected by some hidden surrounding factors (i.e., confounders), leading to biased forecasting models for future data. This paper presents a causal intervention approach to eliminate the bias that is raised by some hidden confounders. By using a causal graph, we illustrate why hidden confounders can bring bias in time series forecasting and how to tackle it. We implement causal intervention by a deep architecture that consists of two modules, a Confounders Estimation module to estimate the hidden confounders and a Debiasing module to eliminate the confounding bias in the forecasting model via sampling on confounders. We conduct comprehensive evaluations on various time series datasets. The experiment results indicate that the proposed method can reduce the negative confounding effects in time series data, and it achieves superior gains over state-of-the-art baselines for time series forecasting.

External IDs:dblp:journals/tkde/GongPLQY25