Forecasting Bitcoin Price Using an Adaptive Grey System Deep Neural Network
| dc.contributor.advisor | Sarkar, Medha | |
| dc.contributor.author | Khaliq, Yousaf | |
| dc.contributor.committeemember | Ranganathan, Jaishree | |
| dc.contributor.committeemember | Sainju, Arpan | |
| dc.date.accessioned | 2022-12-16T23:06:41Z | |
| dc.date.available | 2022-12-16T23:06:41Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2022-12-16T23:06:41Z | |
| dc.description.abstract | Bitcoin was created in 2009 by a person or group of persons under the name Satoshi Nakamoto. Bitcoin trading quickly grew along with the creation of numerous other cryptocurrencies in what is now the crypto market. Linear and non-linear methods have been applied to the prediction of bitcoin price including Support Vector Machines, Autoregressive Integrated Moving Average, Random Forests, and Recurrent Neural Networks among many others. Grey System Theory, developed by Deng Julong in 1982, is a linear forecasting method known for performing well with limited data sets. The aim of this research is to forecast bitcoin price using a non-linear approach that incorporates Grey System Theory. The result is a well generalized non linear model trained on only 60 days of bitcoin price data. | |
| dc.description.degree | M.S. | |
| dc.identifier.uri | https://jewlscholar.mtsu.edu/handle/mtsu/6810 | |
| dc.language.rfc3066 | en | |
| dc.publisher | Middle Tennessee State University | |
| dc.source.uri | http://dissertations.umi.com/mtsu:11672 | |
| dc.subject | Cryptocurrency | |
| dc.subject | Deep Learning | |
| dc.subject | Forecasting | |
| dc.subject | Grey System Theory | |
| dc.subject | Neural Network | |
| dc.subject | Computer science | |
| dc.thesis.degreelevel | masters | |
| dc.title | Forecasting Bitcoin Price Using an Adaptive Grey System Deep Neural Network |