Abstract:
One of the important uses of blockchain technology is cryptocurrencies. Interest in
cryptocurrencies as a financial investment tool has increased recently. With the increasing
interest, many cryptocurrencies have taken place in the market. The most popular among
cryptocurrencies is bitcoin. Predictions and analyzes about the future are important in order to
profit in bitcoin and other cryptocurrencies. For this, different traditional and artificial
intelligence-based methods are used in the literature. One of the artificial intelligence
techniques used is artificial neural networks (ANNs). One of the important processes of the
ANN is the training process. In order to obtain effective results with the ANN, an effective
training algorithm is needed. The flower pollination algorithm (FPA), which models the
pollination process in nature, is one of the popular optimization algorithms. It has been used in
the solution of many problems and has been accepted in the literature. In this study, an approach
based on FPA and ANN is proposed for the prediction of bitcoin price. The weights of the ANN
are determined using the FPA. With the proposed approach, time series analysis is performed
using historical bitcoin prices. Daily bitcoin data between 1 April 2022 and 30 June 2022 is
utilized. The applications are realized on different network structures for effective bitcoin price
prediction. The results show that the proposed approach based on FPA and ANN is effective
for the prediction of bitcoin price.
