Comparative Analysis of Bitcoin Price Prediction Models: LSTM, BiLSTM, ARIMA and Transformers

Abstract

The proposed work explores the significance of Bitcoin in today’s financial landscape and its role as a decentralized store of value and hedge against economic uncertainty. The diverse forecasts for Bitcoin prices are proposed and the importance of accurate prediction models. Specifically, it emphasizes the effectiveness of Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models in capturing the complex dynamics of cryptocurrency markets, offering insights for traders, investors, and researchers. The growing importance of Deep Neural Networks (DNNs) is highlighted by analyzing historical market data and forecasting future price movements. The proposed work concludes by underscoring the evolution of Bitcoin price prediction methodologies from traditional models like ARIMA to advanced techniques like Bi-LSTM, and Auto-regressive EncoderDecoder Transformer, enhancing financial security and decision-making in the cryptocurrency market. The Bi-LSTM worked by providing a 0.9832 R2 score.