Feature Selection for Forecasting

Abstract

This work investigates the importance of feature selection for improving the forecasting performance of machine learning algorithms for financial data. Artificial neural networks (ANN), convolutional neural networks (CNN), long-short term memory (LSTM) networks, as well as linear models were applied for forecasting purposes. The Feature Selection with Annealing (FSA) algorithm was used to select the features from about 1000 possible predictors obtained from 26 technical indicators with specific periods and their lags. In addition to this, the Boruta feature selection algorithm was applied as a baseline feature selection method. The dependent variables consisted of daily logarithmic returns and daily trends of ten financial data sets, including cryptocurrency and different stocks. Experiments indicate that the FSA algorithm increased the performance of ML models regardless of the problem type. The FSA hybrid machine learning models showed better performance in 10 out of 10 data sets for regression and 8 out of 10 data sets for classification. None of the hybrid Boruta models outperformed the hybrid FSA models. However, the BOR-CNN model performance was comparable to the best model for 4 out of 10 data sets for regression estimates. BOR-LR and BOR-CNN models showed comparable performance with the best hybrid FSA models in 2 out of 10 datasets for classification. FSA was observed to improve the model performance in both better performance metrics as well as a decreased computation time by providing a lower dimensional input feature space.

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“…Feature selection is a critical step in the machine learning process that helps identify important variables that contribute significantly to the prediction task. Feature selection plays a vital role in ensuring the success of machine learning models, including, but not limited to the following: Improves Model Performance: By selecting only the most relevant features, the model can perform better and achieve higher accuracy (Pabuccu & Barbu, 2023). Reduces Complexity: Feature selection simplifies the model by eliminating unnecessary features, which reduces computational complexity and improves interpretability (Sisiaridis & Markowitch, 2017).…”