Presenting the Forecasting Model of Bitcoin Return Using the hybrid Method of Deep Learning - Signal Decomposition Algorithm (CEEMD-DL)

With the increasing popularity and widespread use of cryptocurrencies, the creation and development of methods for predicting price movements in this field has attracted a lot of attention. In between, recent developments in deep learning (DL) models with structures such as long-short-term memory (LSTM) and convolutional neural network (CNN) have made improvements in the analysis of this type of data. Another approach that can be effective in the analysis of cryptocurrencies time series is the decomposition through algorithms such as complete integrated empirical mode decomposition (CEEMD). Considering the importance of forecasting in the cryptocurrencies field, in this research, by combining deep learning models and complete integrated empirical mode decomposition (CEEMD), The hybrid CEEMD-DL(LSTM) model has been used to forecast the bitcoin return (as the most popular currency). In this regard, the daily data of the total index of the Tehran Stock Exchange was used in the period of 2013/01/01 – 2022/05/28 and the results obtained were compared with the results of competing models based on efficiency measurement criteria. Based on the obtained results, the use of the introduced model (CEEMD-DL(LSTM)) has increased the efficiency and accuracy of bitcoin return forecasting. Accordingly, the use of this model in this field is suggested.