Bitcoin Daily Price Prediction Using SVR Optimized with WOA
TL;DR
A Support Vector Regression model for predicting the next-day Bitcoin closing price and compares Grid Search tuning with the Whale Optimization Algorithm using weighted Time Series Cross-Validation demonstrate a trade-off between slightly lower test errors and substantially greater optimization cost.
Abstract
Bitcoin is a highly liquid yet volatile crypto asset, making reliable next-day price forecasting challenging. This study develops a Support Vector Regression (SVR) model for predicting the next-day Bitcoin closing price and compares Grid Search tuning with the Whale Optimization Algorithm (WOA) using weighted Time Series Cross-Validation. The dataset comprised 2,620 daily observations from 10 March 2019 to 11 May 2026. Because the final row had no next-day target, it was excluded from scaling, validation, and model selection and retained only for one-step forecasting, leaving 2,619 modelled observations. An 80:20 chronological split produced 2,095 training observations and 524 independent test samples. Grid Search identified C=100, gamma=0.01, and epsilon=0.001 in 8.54 seconds, whereas warm-started WOA identified C=74.21, gamma=0.0075, and epsilon=0.0004 in 220.48 seconds. On the test set, SVR-WOA numerically reduced MAE from USD 1,612.09 to USD 1,551.35, RMSE from USD 2,161.85 to USD 2,112.09, and MAPE from 1.72% to 1.67%, while R² increased from 0.9803 to 0.9812. These differences correspond to improvements of 3.77%, 2.30%, and 3.05%, respectively, but WOA required approximately 25.8 times longer optimization. Because WOA was executed once and evaluated on a single chronological test split, the observed gains should be interpreted as numerical improvements rather than statistically established superiority. The findings therefore demonstrate a trade-off between slightly lower test errors and substantially greater optimization cost.