College - Author 1
College of Engineering
Department - Author 1
Electrical Engineering Department
Degree Name - Author 1
BS in Electrical Engineering
Date
12-2019
Primary Advisor
Xiao-Hua (Helen) Yu, College of Engineering, Electrical Engineering Department
Abstract/Summary
In this project, I will investigate the performance of several major neural network architectures for the task of Bitcoin price prediction. Bitcoin is a cryptocurrency that is recently becoming increasingly more popular, and more widely adopted as a financial instrument. As a result, more efforts have been made in the past several years to model and predict its price. However, to this moment a large portion of work on Bitcoin price modeling was done using statistical or classical machine learning techniques. At the same time, other artificial intelligence based prediction techniques, and specifically neural networks, have not been explored to the same extent.
Further, multi-layer perceptron (MLP), recurrent neural networks (RNNs), and convolutional neural networks (CNNs) that are currently successfully applied in many fields of engineering and science are often overlooked when it comes to financial time series modeling. Thus, the main goal of the project is to partially fill in this research gap by evaluating the performance of the three widely used neural network architectures – MLP, RNN and CNN, in the task of Bitcoin price prediction.
URL: https://digitalcommons.calpoly.edu/eesp/466
Supplemental Project Files
Included in
Computational Engineering Commons, Controls and Control Theory Commons, Other Computer Engineering Commons, Other Electrical and Computer Engineering Commons