Analysis of different artificial neural networks for Bitcoin price prediction

Predicting the future price of the currency has always been considered one of the most challenging issues. In this paper, we utilize different artificial neural networks (ANNs), including Feedforwardnet, Fitnet, and Cascade networks, and predict the future price of Bitcoin. This paper discusses how a combination of technical attributes, like price-related and lagged features, as inputs of the neural networks, are used to raise the prediction capabilities that directly impact into the final profitability. For empirical analysis, this paper uses the data of the Bitcoin price for a period of 9 months (1.1.2018 - 30.9.2018) available on www.coindesk.com. Using a ten-fold cross-validation method, this paper finds the optimal number of hidden neurons for different train functions in each ANN based on error measures, including mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Then, the Bitcoin price is predicted, and results are compared based on the amount of R to find out which ANN leads to a better prediction. Finally, this paper concludes that the Fitnet network with trainlm function and 30 hidden neurons outweighs the others. This paper assesses the models' performance and how specific setups produce principled and stable predictions for beneficial trading.