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Abstract
Bitcoin has recently been greatly regarded as an investment asset. It is incredibly unpredictable despite being the biggest digital currency. Therefore, accurate forecasting is essential for making investment strategies. This is a difficulty that the latest research effort takes on to construct a revolutionary Bitcoin price prediction model by incorporating new feature engineering and price prediction methods. The original features are first retrieved from the actual Bitcoin data obtained. This work is well-fit and accurate by developing a novel feature computing framework. The proposed decomposed inter-day difference based features and the second order technical indicator are generated within the feature extraction stage. Following that, the developed two-level ensemble classifier is used to accurately forecast the Bitcoin price value using extracted and original features. The two-level ensemble classifier blends the outstanding classifiers support vector machine and artificial neural networks. It is intended to adjust the weight parameter throughout training the ensemble method to accommodate the unpredictability features of Bitcoin prices better. The article presented the novel self-adaptive bat algorithm as a solution. With regard to specific performance metrics, the output of the two-level ensemble classifier is contrasted with that of the current models.