When Exogenous Features Hurt: Rethinking Feature Selection in Time-Series Forecasting

Feature selection plays a critical role in machine learning in improving generalization and interpretability. In time series forecasting, this problem is particularly challenging: predicting a target variable often relies on a diverse set of exogenous features, yet recent studies have shown that incorporating certain features can unexpectedly degrade performance. This counterintuitive phenomenon has led to the growing adoption of univariate or channel-independent approaches, raising a fundamental question: when and why are exogenous features indeed beneficial? In this work, we argue that effective feature selection should be guided not by static relevance (e.g., correlation), but by the temporal stability of the conditional relationship between features and the target. We introduce Conditional Distribution Difference (CDD), a tractable proxy that characterizes temporal instability in the conditional relationship through a bin-wise representation. Based on this representation, we derive CDD-std, the standard deviation of CDD across time, as a feature selection metric that quantifies conditional stability. Our key insight is that features with high CDD-std and unstable conditional relationships are difficult to generalize and tend to harm forecasting performance. We validate this insight on controlled synthetic experiments and real-world datasets, showing that CDD-std provides a more effective indicator of feature usefulness than correlation-based metrics. We develop simple yet effective feature selection strategies building on CDD-std, including hard filtering and TimeLess, achieving up to 18% performance improvement over state-of-the-art forecasting models across diverse settings.