The Future of Bitcoin Price Predictions Integrating Deep Learning and the Hybrid Model Method
Annotatsiya
Over the past few decades, recurrent neural networks, particularly the Long Short-Term Memory (LSTM) architecture, have undergone several refinements. These networks have emerged as the go-to models for numerous machine learning challenges, especially those involving sequential data. One such application is the prediction of Bitcoin prices, a cryptocurrency that stands at the forefront of blockchain technology. This paper delves into the intricacies of forecasting Bitcoin prices using a suite of models, with a keen emphasis on the LSTM architecture, renowned for its prowess in handling tasks with long-term dependencies. Our exploration encompasses traditional time series models like ARIMA, neural network variants such as ANN and Transformer-based models, and even hybrid combinations. Specifically, our LSTM model, augmented with peephole connections, demonstrates its capability to learn and predict Bitcoin price fluctuations. We source our data from the Bitcoin Price Index and aim to gauge the accuracy with which these models can predict Bitcoin's price trajectory. Furthermore, our experiments involve the deployment of an "adam"-optimized Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network, revealing insights into their predictive performances.