Forecasting bitcoin price considering macro economic factors and media influence using bidirectional LSTM and random forest regressor as ensemble model
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
| Main Authors: | , , , , |
|---|---|
| 其他作者: | |
| 格式: | Thesis |
| 語言: | English |
| 出版: |
Brac University
2023
|
| 主題: | |
| 在線閱讀: | http://hdl.handle.net/10361/19963 |
| id |
10361-19963 |
|---|---|
| record_format |
dspace |
| spelling |
10361-199632024-03-13T21:01:14Z Forecasting bitcoin price considering macro economic factors and media influence using bidirectional LSTM and random forest regressor as ensemble model Ghose, Swattic Bin Khaled, Faiyaz Rafin, Nafiz Imtiaz Jawwad, Rubaiyet Hossain Bin Yahiya, Yamin Rabiul Alam, Dr. Md. Golam Department of Computer Science and Engineering, Brac University Bitcoin Macro economic Sentiment analysis GPU Bayesian optimization Bidirectional LSTM Ensemble random forest regressor Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-41). The decentralized cryptocurrency has created many opportunities for secure and safe financial transactions with a bright prospect. The cryptocurrency market rapidly expands, leading to erratic price movements due to geopolitical, social, and other macroeconomic factors. As a result, the price of such cryptocurrencies changes every day. For our research, we limit our scope to predicting and forecasting bitcoin prices accurately. For predicting the trend of Bitcoin price, we considered two major fac tors: the consideration of various macroeconomic markets and the sentiment analysis of social media. Our contribution to this research was the volume of data that we collected for sentiment analysis for tweets which is approximately 85 millions. In addition, we considered the impact of the markets of AMD and NVIDIA which are the main tech companies that provide consumer level GPU that has a huge impact in cryptocurrency mining, which has never been considered before for predicting cryptocurrency prices and to improve our accuracy we used ensemble Random For est Regression with Bidirectional LSTM. In this case, we considered Twitter. We have used the Vader Sentiment Analysis model to calculate the sentiment scores (positive, negative, neutral, and compound). We have used four parallel Bayesian Optimized Bi-LSTM models, each with its input features, to combine their predic tions and train an ensemble Random Forest Regressor with those predictions. Then, we used the trained RFR model to pick the best forecast out of those four parallel Bi-LSTM models. Furthermore, we got the following results: MSE = 0.0021607, MAE = 0.0318709, R2 = 0.99909, and MAPE = 0.0038217. The findings were that Bidirectional LSTM functions better in prediction when we consider sentiment anal ysis and other macroeconomic factors(AMD, NVIDIA, S&P 500, NASDAQ, GOLD stock prices). Moreover, using RFR as an ensemble model, the accuracy is boosted significantly. Swattic Ghose Faiyaz Bin Khaled Nafiz Imtiaz Rafin Rubaiyet Hossain Jawwad Yamin Bin Yahiya B. Computer Science and Engineering 2023-08-27T08:35:26Z 2023-08-27T08:35:26Z 2023 2023-01 Thesis ID: 19101216 ID: 19101138 ID: 19101169 ID: 19101079 ID: 19101274 http://hdl.handle.net/10361/19963 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 41 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Bitcoin Macro economic Sentiment analysis GPU Bayesian optimization Bidirectional LSTM Ensemble random forest regressor Machine learning |
| spellingShingle |
Bitcoin Macro economic Sentiment analysis GPU Bayesian optimization Bidirectional LSTM Ensemble random forest regressor Machine learning Ghose, Swattic Bin Khaled, Faiyaz Rafin, Nafiz Imtiaz Jawwad, Rubaiyet Hossain Bin Yahiya, Yamin Forecasting bitcoin price considering macro economic factors and media influence using bidirectional LSTM and random forest regressor as ensemble model |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Rabiul Alam, Dr. Md. Golam |
| author_facet |
Rabiul Alam, Dr. Md. Golam Ghose, Swattic Bin Khaled, Faiyaz Rafin, Nafiz Imtiaz Jawwad, Rubaiyet Hossain Bin Yahiya, Yamin |
| format |
Thesis |
| author |
Ghose, Swattic Bin Khaled, Faiyaz Rafin, Nafiz Imtiaz Jawwad, Rubaiyet Hossain Bin Yahiya, Yamin |
| author_sort |
Ghose, Swattic |
| title |
Forecasting bitcoin price considering macro economic factors and media influence using bidirectional LSTM and random forest regressor as ensemble model |
| title_short |
Forecasting bitcoin price considering macro economic factors and media influence using bidirectional LSTM and random forest regressor as ensemble model |
| title_full |
Forecasting bitcoin price considering macro economic factors and media influence using bidirectional LSTM and random forest regressor as ensemble model |
| title_fullStr |
Forecasting bitcoin price considering macro economic factors and media influence using bidirectional LSTM and random forest regressor as ensemble model |
| title_full_unstemmed |
Forecasting bitcoin price considering macro economic factors and media influence using bidirectional LSTM and random forest regressor as ensemble model |
| title_sort |
forecasting bitcoin price considering macro economic factors and media influence using bidirectional lstm and random forest regressor as ensemble model |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/19963 |
| work_keys_str_mv |
AT ghoseswattic forecastingbitcoinpriceconsideringmacroeconomicfactorsandmediainfluenceusingbidirectionallstmandrandomforestregressorasensemblemodel AT binkhaledfaiyaz forecastingbitcoinpriceconsideringmacroeconomicfactorsandmediainfluenceusingbidirectionallstmandrandomforestregressorasensemblemodel AT rafinnafizimtiaz forecastingbitcoinpriceconsideringmacroeconomicfactorsandmediainfluenceusingbidirectionallstmandrandomforestregressorasensemblemodel AT jawwadrubaiyethossain forecastingbitcoinpriceconsideringmacroeconomicfactorsandmediainfluenceusingbidirectionallstmandrandomforestregressorasensemblemodel AT binyahiyayamin forecastingbitcoinpriceconsideringmacroeconomicfactorsandmediainfluenceusingbidirectionallstmandrandomforestregressorasensemblemodel |
| _version_ |
1814308633190596608 |