Performance Evaluation of a Feature-Importance-based Feature Selection Method for Time Series Prediction
Hyun Ahn · Journal of information and communication convergence engineering · 2023
Various machine-learning models may yield high predictive power for massive time series for time series prediction.However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings.Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction.We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset.First, we ranked the features using impurity-and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models.Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially.Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.