Multi-Objective Lagged Feature Selection Based on Dependence Coefficient for Time-Series Forecasting

In the fast-evolving field of machine learning, the process of feature selection is essential for reducing model complexity and enhancing interpretability. Within this context, filter methods have gained recognition for their effectiveness in assessing features through statistical metrics. A recently introduced metric, the Conditional Dependence Coefficient, aims to assess the dependence between subsets of features and a target variable, enhancing our understanding of feature relevance. This paper presents a novel feature selection approach that integrates this statistical metric with a multi-objective evolutionary algorithm. This strategy leverages the flexibility of evolutionary algorithms to efficiently explore the feature space and employs an intuitive metric for identifying pertinent features. Unlike many filter-based approaches, our method does not require thresholds or percentiles related to the number of selected features and evaluates the collective merit of feature subsets instead of the significance of individual features. To address the forecasting challenge of identifying the appropriate time lags and features, we performed experiments on eight distinct datasets containing multivariate time-series data. Comparing our method against a baseline with no feature selection, our results show solid performance in efficacy and a notable reduction in model complexity.