Forecasting using predictor selection from a large set of highly correlated variables

Abstract

The potential of correlation-based feature selection has been explored in selecting an optimal subset from a set of highly correlated predictors. This problem occurs, for example, in time series forecasting of economic indicators using regression models on multiple lags of a large number of candidate leading indicators. Greedy algorithms (forward selection and backward elimination) in such cases fail. To obtain the globally optimal solution, the feature selection problem is formulated as a mixed integer programming problem. To solve it, we use the binary cut-and-branch method. The results of simulation studies demonstrate the advantage of using the binary cut-and-branch method in comparison with heuristic search algorithms. The real example of the selection of leading indicators of consumer price index growth shows the acceptability of using the correlation-based feature selection method.