Abstract
Bitcoin has been the most used blockchain platform in business and finance in recent years. This paper aims to find a reliable prediction model that improves a combination of prediction models. Exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and forecasts combination models are among the techniques used in this Paper. The effect of artificial intelligence models in enhancing the results of compound prediction models is the study’s most obvious finding. The second major finding was that a model of a robust combination forecasting model that responds to the many variations that occur in the bitcoin time series and Error improvement should be adopted. The results of the prediction accuracy criterion and matching curve fitting in this paper showed that if the residuals of the changed model are white noise, the forecasts are unbiased. A future study investigating robust combination forecasting would be very interesting.
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Ashour, M.A.H., Aldahhan, I.A.H. (2023). Optimal Combination Forecast for Bitcoin Dollars Time Series. In: Valenzuela, O., Rojas, F., Herrera, L.J., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis and Forecasting. ITISE 2021. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-14197-3_11
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