Integrating Moving Average Indicators with Long Short-Term Memory Model in Bitcoin Price Forecasting

Abstract

Bitcoin price forecasting remains a challenging task due to the market's high volatility and complex nonlinear dynamics. This study proposes a novel forecasting framework by integrating Long Short-Term Memory (LSTM) networks with Moving Average (MA) indicators—specifically Simple Moving Average (SMA), Exponential Moving Average (EMA), and Weighted Moving Average (WMA)—as auxiliary input features to enhance model accuracy. The objective is to examine the frequency-specific effectiveness of these hybrid models across daily and high-frequency datasets. Using historical Bitcoin data from Bitstamp between January 2021 and December 2024, we conducted experiments at four epoch levels (50, 100, 150, 200) to determine optimal model configurations. Empirical results reveal that, on daily data, LSTM combined with a 10-period WMA achieves the lowest Mean Absolute Percentage Error (MAPE) of 2.1661% at 150 epochs, while for high-frequency data, the combination with a 10-period SMA yields superior performance with a MAPE of 0.4895%. Furthermore, increasing epochs beyond the optimal point led to performance degradation, indicating overfitting. Compared to the standalone LSTM model, our integrated approach demonstrates significantly improved adaptability to short-term fluctuations and heightened forecasting precision. This research contributes a comprehensive comparative analysis of MA-enhanced deep learning models for cryptocurrency price prediction, and offers practical insights for algorithmic traders, financial analysts, and decision-support systems in volatile digital asset markets.