(untitled) (Interdisciplinary Studies Journal, 2026)
This study investigated the predictive performance of WEKA software in Bitcoin (BTC) time series forecasting, comparing its results with those obtained through R using historical data from May 2013 to December 2024 (4,254 observations). The analysis employed multiple machine learning algorithms evaluated through MAPE, RMSE, and MAE measures. Results revealed that while R generally achieved superior performance across most models, particularly in terms of error reduction, WEKA showed competitive and in some cases, superior performance in specific models: M5Rules reached MAPE of 2.25% and RMSE of 1,449.49, while SMOreg obtained MAPE of 2.40% and RMSE of 1,450.25 in the Forecast tab. Rule-based models, M5Rules and M5P, exhibited notably stronger performance in R, with MAPE of 3.79% and RMSE of 2,112.25, compared to modest results in WEKA where they showed correlation near zero in the Classify tab. The KPSS test confirmed series non-stationarity (p-value = 0.01), and Granger causality test identified significant temporal dependence at lag 5 (p-value = 8.7e-06). Findings indicate that while WEKA offers adequate functionality for specific models in sequential forecasting tasks, it exhibits instabilities and limitations in comprehensive temporal modeling, making R a more robust alternative for complex financial applications.