• Energy Research
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Abstract The price forecasting of the digital currencies in the financial market is of great importance, especially after the recent global economic crises. Due to the nonlinear dynamics, which is including inherent fractality and chaoticity of the digital currencies, it is understood from the research conducted by many researchers that a single model is not sufficient in forecasting the digital currencies with very high accuracy. Since the single models used in the forecasting of digital currencies have weaknesses as well as their own strengths, they might not grant the best forecasting achievement in all situations for all the time. A new hybrid-forecasting framework has been proposed in digital currency time-series to minimize this negative situation and increase forecasting achievement. In this study, a novel hybrid forecasting model based on long short-term memory (LSTM) neural network and empirical wavelet transform (EWT) decomposition along with cuckoo search (CS) algorithm is developed for digital currency time series. The model is obtained by combining the LSTM neural network and EWT decomposition technique, and optimizing the intrinsic mode function (IMF) estimated outputs with CS. The price of the four most traded digital currencies such as BTC, XRP, DASH and LTC, is estimated by the proposed model and the performance of the model has been tested. The experimental results show that the hybrid model proposed for digital currency forecasting can capture nonlinear properties of digital currency time series.