Leveraging Machine Learning for Cryptocurrency Price Forecasting
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Abstract
Digital currencies are increasingly recognized as viable alternatives to traditional fiat money, with cryptocurrency trading emerging as an enticing avenue for investors seeking potentially lucrative opportunities. Accurate price prediction holds paramount importance in optimizing returns on cryptocurrency investments, given the intricate nature of price fluctuations over time. In response to this imperative, we introduce a novel hybrid deep learning model, which combines the capabilities of a one-dimensional convolutional neural network (1DCNN) and a Stacked Gated Recurrent Unit (GRU). This hybrid architecture effectively encodes historical cryptocurrency price data into a meaningful high-level representation through 1DCNN and captures long-term dependencies within the data using the Stacked GRU component. Our comprehensive evaluation, conducted across three diverse cryptocurrency datasets (Bitcoin, Ethereum, and Ripple), demonstrates the superior performance of the proposed 1DCNN-GRU model, showcasing its ability to outperform existing techniques with notably reduced Root Mean Square Error (RMSE) values: 43.933 for Bitcoin, 3.511 for Ethereum, and 0.00128 for Ripple.
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