Faculty Supervisor
Dr. Haya El Ghalayini
Date of Defense
Fall 12-12-2020
Program Name
Honours Bachelor of Computer Science (Mobile Computing)
School
Applied Computing
Keywords
machine learning, bitcoin, time series forecasting, N-BEATS, deep learning
Department
Faculty of Applied Science & Technology (FAST)
Description
This study evaluates the predictive power of the N-BEATS deep learning architecture trained on Bitcoin daily, hourly, and up-to-the-minute data in comparison with other popular time series forecasting methods such as LSTM and ARIMA.
Abstract
The use of computationally intensive systems that employ machine learning algorithms is increasingly common in the field of finance. New state of the art deep learning architectures for time series forecasting are being developed each year making them more accurate than ever. This study evaluates the predictive power of the N-BEATS deep learning architecture trained on Bitcoin daily, hourly, and up-to-the-minute data in comparison with other popular time series forecasting methods such as LSTM and ARIMA. Prediction errors are measured with Mean Average Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results suggest that the developed N-BEATS model has promising predictive power compared to LSTM and ARIMA models.
Copyright
© Alikhan Bulatov
Terms of Use
Terms of Use for Works posted in SOURCE.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Bulatov, Alikhan, "Forecasting Bitcoin Prices Using N-BEATS Deep Learning Architecture" (2020). Student Theses. 5.
https://source.sheridancollege.ca/fast_sw_mobile_computing_theses/5