Time Series Forecasting with General Regression Neural Network - Hric, Patrik; Šafr, Karel; Čabla, Adam, 1985-; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (2025)
This thesis presents a forecasting methodology for BTC-USD Close Prices based on General Regression Neural Networks (GRNN), a class of nonparametric models designed to estimate complex functional relationships from data. The proposed approach addresses limitations in predictive performance through a multi-stage modeling pipeline incorporating lagged in- put features, a recency-sensitive weighting vector to emphasize short-term dynamics, and convolution-based transformations to construct higher-level temporal representations. Fore- casting is conducted using a recursive one-step-ahead strategy and evaluated via root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The GRNN model is benchmarked against the Automatic ARIMA framework to assess comparative forecasting accuracy under real-market conditions. Results indicate that the enhanced GRNN architecture achieves performance on par with the benchmark while offering greater flexibility in adapting to nonstationary and structurally evolving data.
Vedoucí práce: Karel Šafr
Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2025
Způsob přístupu: Internet
Keywords: statistika [obor dipl. práce]; diplomové práce; master's theses