Predicting the Demand in Bitcoin Using Data Charts: A Convolutional Neural Networks Prediction Model

Abstract

Traditional time series modeling techniques emphasize on predicting cryptocurrencies using classically structured data representation as numerical features to present the time-series datasets. In this paper, a novel approach to analyze time-series data charts using a modified Convolutional Neural Networks (CNNs) is proposed. The CNNs have been adopted to recognize subtle and undetectable patterns within images of time-series data charts. Our approach has been proven to achieve significant results, suggesting a need for further research into this new method for time series modeling, especially for Bitcoin.