Optimizing Forecast Accuracy in Cryptocurrency Markets: Evaluating Feature Selection Techniques for Technical Indicators

This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators. In this work, over 130 technical indicators—covering momentum, volatility, volume, and trend-related technical indicators—are subjected to three distinct feature selection approaches. Specifically, mutual information (MI), recursive feature elimination (RFE), and random forest importance (RFI). By extracting an optimal set of 20 predictors, the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability. These feature subsets are integrated into support vector regression (SVR), Huber regressors, and k-nearest neighbors (KNN) models to forecast the prices of three leading cryptocurrencies—Bitcoin (BTC/USDT), Ethereum (ETH/USDT), and Binance Coin (BNB/USDT)—across horizons ranging from 1 to 20 days. Model evaluation employs the coefficient of determination (R2) and the root mean squared logarithmic error (RMSLE), alongside a walk-forward validation scheme to approximate real-world trading contexts. Empirical results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy, with particularly pronounced effects observed at longer forecast windows. Moreover, indicators related to volume and trend provide incremental benefits in select market conditions. Notably, an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator set. These findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model robustness. This research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction horizons. The outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resilient forecasting algorithms. Future efforts should incorporate high-frequency data and explore alternative selection techniques to further refine predictive accuracy in this highly volatile domain.