A SYSTEMATIC LITERATURE REVIEW OF LSTM NETWORKS FOR BITCOIN PRICE FORECASTING PERFORMANCE

Abstract

Bitcoin price forecasting remains a significant challenge in financial analytics due to the highly volatile and nonlinear nature of cryptocurrency markets. Traditional forecasting techniques, such as Autoregressive Integrated Moving Average (ARIMA) and Linear Regression, often struggle to capture the complex temporal relationships present in Bitcoin price movements. In recent years, deep learning approaches, particularly Long Short-Term Memory (LSTM) networks, have gained considerable attention because of their ability to model sequential data and learn long-term dependencies. This study presents a Systematic Literature Review (SLR) of the performance of pure LSTM networks in Bitcoin price forecasting. Following the PRISMA framework, 60 peer-reviewed studies published between 2020 and 2026 were systematically identified and analyzed from major academic databases, including Google Scholar, IEEE Xplore, ScienceDirect, and SpringerLink. The review evaluates forecasting accuracy, methodological consistency, interpretability, and the effectiveness of standardized OHLCV (Open, High, Low, Close, and Volume) data in prediction tasks. The findings indicate that pure LSTM models generally outperform traditional econometric methods in highly volatile market conditions due to their gated memory architecture, which effectively captures long-term temporal patterns. The study highlights the potential of LSTM as a reliable and interpretable forecasting approach and provides a benchmark framework for future research in cryptocurrency forecasting and artificial intelligence-driven financial analytics.