Modeling and prediction of bitcoin prices based on blockchain information

Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology, monetary units and blockchain. This paper reveals the effect of neural networks (NNs) by analyzing the time series of the Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin’s supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. In this paper, the purpose of Bitcoin price prediction is to use the combined method of signal decomposition into intrinsic components (EMD) and support vector regression (SVR) algorithms. The proposed method uses the intrinsic component decomposition method as a denoising step in the training data. We conduct an empirical study that compares the proposed method with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that NN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price also the Mean Square Error (MSE) of the proposed method is calculated and compared with previous works.