Abstract
The accurate prediction of the Bitcoin price can provide decision support for investors and a reference for governments to make regulatory policies. The Bitcoin price prediction requires a careful analysis and representation due to its data characteristics such as highly volatile, highly non-linear, non-stationary, non-linear dynamics, no periodicity, and existence of spectrum of scaling components, noisy data, and randomness. The price can be effectively forecasted by transforming the original data into another amenable form along with AI tools. In this paper, we used Interval Graph (IG) for transforming original data which is amenable for applying Artificial Neural Networks (ANN) model to predict Bitcoin price. The Bitcoin price, which is a time-series data, is captured in the form of windows representing price of day, week, and month, respectively. We have used three evaluation metrics, such as MAPE, RMSE, and Dstat. The empirical study has clearly demonstrated the encouraging performance and effectiveness of the IG-ANN. The performance is compared with traditional ANN techniques on bitcoin time-series data spanning 2013-2019 and found that IG-ANN is outperforming all.