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https://doi.org/10.4314/jfas.v9i3s.61

has been cited by the following article:

• TITLE: Bitcoin Price Prediction Based on Deep Learning Methods

  AUTHORS: Xiangxi Jiang

  KEYWORDS: Deep Learning Model, Multi-Layer Perceptron, Gated Recurrent Network, Long Short-Term Memory, Cross-Validation, Normalization

  JOURNAL NAME: Journal of Mathematical Finance, Vol.10 No.1, February 10, 2020

  ABSTRACT: Bitcoin is a current popular cryptocurrency with a promising future. It’s like a stock market with time series, the series of indexed data points. We looked at different deep learning networks and methods of improving the accuracy, including min-max normalization, Adam optimizer and windows min-max normalization. We gathered data on the Bitcoin price per minute, and we rearranged them to reflect Bitcoin price in hours, a total of 56,832 points. We took 24 hours of data as input and output the Bitcoin price of the next hour. We compared the different models and found that the lack of memory means that Multi-Layer Perceptron (MLP) is ill-suited for the case of predicting price based on current trend. Long Short-Term Memory (LSTM) provides relatively the best prediction when past memory and Gated Recurrent Network (GRU) is included in the model.