Comparative Evaluation of ARIMA, LSTM, Hybrid ARIMA-GARCH, and Hybrid GARCH-LSTM Models for Daily Bitcoin and Gold Price Forecasting - Nurul Fatatik, Isna; Nur Fadhilah, Asyifa; Adi Nugroho, Irfan; Muflih Affandi, Muhammad; Diah Novita Sari, Vriska; Zuhdi, Shaifudin (Jurnal Teknik Informatika (Jutif), 2026)
The volatile nature of digital financial markets poses major challenges for predictive modelling, particularly in developing accurate forecasting models that can address diverse asset characteristics such as Bitcoin, with its extreme fluctuations, and Gold, which is known for its stable movements. This study addresses this challenge by evaluating the robustness of linear, deep learning, and hybrid architectures in both high-volatility and stable asset environments. Utilizing Bitcoin and Gold closing price data from 2022 to 2025, the methodology adopts a comparative workflow that involves ARIMA, ARIMA-GARCH, LSTM, and LSTM-GARCH Hybrid models. Stationarity (ADF) and heteroskedasticity (ARCH-LM) diagnostics alongside AIC/BIC selection criteria were applied, followed by a walk-forward validation scheme to assess the model's performance. Results confirmed that the hybrid GARCH-LSTM model delivered the lowest Root Mean Squared Error (RMSE), significantly outperforming single models by integrating statistical variance and temporal neural learning. Therefore, this study contributes to the field of computational intelligence by validating an accurate Artificial Intelligence (AI) framework for volatility-based forecasting and proposing a scalable blueprint for engineers to develop models that are capable of capturing the dynamics of financial time series data.