Cryptocurrency Price Prediction Using Frequency Decomposition and Deep Learning

C. Jin · Yong Li

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Stock Market Forecasting Methods Blockchain Technology Applications and Security Time Series Analysis and Forecasting

Abstract

Given the substantial volatility and non-stationarity of cryptocurrency prices, forecasting them has become a complex task within the realm of financial time series analysis. This study introduces an innovative hybrid prediction model, VMD-AGRU-RESVMD-LSTM, which amalgamates the disintegration–integration framework with deep learning techniques for accurate cryptocurrency price prediction. The process begins by decomposing the cryptocurrency price series into a finite number of subseries, each characterized by relatively simple volatility patterns, using the variational mode decomposition (VMD) method. Next, the gated recurrent unit (GRU) neural network, in combination with an attention mechanism, predicts each modal component’s sequence separately. Additionally, the residual sequence, obtained after decomposition, undergoes further decomposition. The resultant residual sequence components serve as input to an attentive GRU (AGRU) network, which predicts the residual sequence’s future values. Ultimately, the long short-term memory (LSTM) neural network integrates the predictions of modal components and residuals to yield the final forecasted price. Empirical results obtained for daily Bitcoin and Ethereum data exhibit promising performance metrics. The root mean square error (RMSE) is reported as 50.651 and 2.873, the mean absolute error (MAE) stands at 42.298 and 2.410, and the mean absolute percentage error (MAPE) is recorded at 0.394% and 0.757%, respectively. Notably, the predictive outcomes of the VMD-AGRU-RESVMD-LSTM model surpass those of standalone LSTM and GRU models, as well as other hybrid models, confirming its superior performance in cryptocurrency price forecasting.

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2015Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin 2017Exploring the Dynamic Relationships between Cryptocurrencies and Other Financial Assets 2018Financial time series forecasting model based on CEEMDAN and LSTM 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm 2018Predicting the Price of Bitcoin Using Machine Learning 2020Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis 2022Carbon price forecasting based on CEEMDAN and LSTM 2020Stock price prediction using deep learning and frequency decomposition

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