Rabiatul Adawiyah

Full Author List

Lahuddin, Harlinda; Muliawan, Muh. Raihan Alif; Darwis; Herdianti; Jabir, Sitti Rahmah; Adawiyah, Rabiatul, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Takemoto, Kazuhiro, Department of Bioscience and Bioinformatics, Kyushu Institute of Technology Fukuoka, Japan

Abstract

Cryptocurrency markets exhibit significant volatility and nonlinearity, creating difficulties for precise price prediction. This study assesses and contrasts six deep learning models LSTM, CNN, Transformer, TCN, CNN-LSTM, and TCN-LSTM for forecasting the closing prices of four popular cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), and Litecoin (LTC). Each model utilizes historical OHLCV data of 5 years, processed through a uniform preprocessing pipeline that encompasses normalization, sliding window segmentation, and an 80: 20 train-test division. Experimental findings indicate that the hybrid TCN-LSTM model exceeds the performance of all other models based on evaluation metrics including MAE, RMSE, MAPE, and \boldsymbol{R}^{2}, showing its capacity to grasp both short- and long-term time patterns. This study additionally highlights the effectiveness of parallel hybrid architectures, particularly the TCN-LSTM model. These findings contribute to the expanding body of research on deep learning applications in financial forecasting and offer practical guidance for developing robust cryptocurrency prediction models.

Related SDGs Targets