Learning to predict cryptocurrency price using artificial neural network models of time series

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dc.contributor.author Gullapalli, Sneha
dc.date.accessioned 2018-04-20T19:14:33Z
dc.date.available 2018-04-20T19:14:33Z
dc.date.issued 2018-05-01 en_US
dc.identifier.uri http://hdl.handle.net/2097/38867
dc.description.abstract Cryptocurrencies are digital currencies that have garnered significant investor attention in the financial markets. The aim of this project is to predict the daily price, particularly the daily high and closing price, of the cryptocurrency Bitcoin. This plays a vital role in making trading decisions. There exist various factors which affect the price of Bitcoin, thereby making price prediction a complex and technically challenging task. To perform prediction, we trained temporal neural networks such as time-delay neural networks (TDNN) and recurrent neural networks (RNN) on historical time series – that is, past prices of Bitcoin over several years. Features such as the opening price, highest price, lowest price, closing price, and volume of a currency over several preceding quarters were taken into consideration so as to predict the highest and closing price of the next day. We designed and implemented TDNNs and RNNs using the NeuroSolutions artificial neural network (ANN) development environment to build predictive models and evaluated them by computing various measures such as the MSE (mean square error), NMSE (normalized mean square error), and r (Pearson’s correlation coefficient) on a continuation of the training data from each time series, held out for validation. en_US
dc.language.iso en en_US
dc.subject Cryptocurrency en_US
dc.subject Artificial neural networks en_US
dc.subject Time series analysis en_US
dc.subject Machine learning en_US
dc.title Learning to predict cryptocurrency price using artificial neural network models of time series en_US
dc.type Report en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Computer Science en_US
dc.description.advisor William Hsu en_US
dc.date.published 2018 en_US
dc.date.graduationmonth May en_US

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