Error Reduction in Cryptocurrency Time Series Forecasting through Bidirectional LSTM and GRU Deep Neural Networks
Subject Areas : مهندسی برق و کامپیوترF. Kazem zadeh 1 , M. Houshmand Kafashian 2 , M. Houshmand 3 *
1 - Imam Reza International University
2 - Telecommunication Company of Iran
3 - Imam Reza International University
Keywords: Time series forecasting, deep learning, bidirectional neural network, cryptocurrency price prediction, neural network prediction error, price prediction error.,
Abstract :
Time series forecasting in engineering, telecommunications, and finance is of great importance. Financial time series, which are often multivariate, require precise and optimized algorithms. In recent research, deep neural networks have demonstrated successful results in improving the accuracy of financial time series forecasting. This study investigates the use of LSTM and GRU networks in predicting cryptocurrency prices and examines the bidirectional implementation of these networks, with an emphasis on optimal hyperparameter selection to reduce prediction error and enhance accuracy through Grid Search, RandomizedSearchCV, and Bayesian methods. Simulation results indicate that employing bidirectional LSTM and BiGRU networks reduced the prediction error rate for BTC by up to 3.22%, for ETH by 3.94%, and for LTC by 3.99%.
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