A hybrid approach for forecasting bitcoin series

【Author】 Mtiraoui, Amine; Boubaker, Heni; BelKacem, Lotfi

【Source】RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE

【影响因子】6.143

【Abstract】Bitcoin price prediction is a substantial challenge for cryptocurrency investors. This study offers an innovative scheme to predict Bitcoin returns and volatilities using a hybrid model that incorporates the autoregressive fractionally integrated moving average (ARFIMA), empirical wavelet (EW) transform, and local linear wavelet neural network (LLWNN) approaches to produce an ARFIMA-EWLLWNN model. Our methodologies integrate the advantages of the long memory model, EW decomposition technique, artificial neural network structure, and back propagation and particle swarm optimization learning algorithms. The experimental results of the optimized hybrid approach outperform some classic models by providing more accurate out-of sample forecasts over longer horizons. The model proves to be the most appropriate Bitcoin forecasting technique. Moreover, the implemented method produces smaller prediction errors than other computing techniques.

【Keywords】Artificial neural networks; Bitcoin; Empirical wavelet transform; Forecast performance; Long-memory process

【发表时间】2023 OCT

【收录时间】2023-08-05

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-价格预测