| Title |
Sustainable Stock Market Prediction Framework Using Machine Learning Models |
| ID_Doc |
72833 |
| Authors |
Peñalvo, FJG; Maan, T; Singh, SK; Kumar, S; Arya, V; Chui, KT; Singh, GP |
| Title |
Sustainable Stock Market Prediction Framework Using Machine Learning Models |
| Year |
2022 |
| Published |
International Journal Of Software Science And Computational Intelligence-Ijssci, 14, 1 |
| DOI |
10.4018/IJSSCI.313593 |
| Abstract |
Prediction of stock prices is a challenging task owing to its volatile and constantly fluctuating nature. Stock price prediction has sparked the interest of various investors, data analysists, and researchers because of high returns on their investments. A sustainable framework for stock price prediction is proposed to quantify the factors affecting the stock price and impact of technology on the ever-changing business world. The proposed framework also helps to understand how technology can be used to predict the future price of stocks by using some historical dataset to produce desirable results using machine learning algorithms. The aim of this research paper is to learn about stock price prediction by using different machine learning algorithms and comparing their performance. The results reveal that Fb-prophet should be preferred for more precise prediction among different ML algorithms. |
| Author Keywords |
Comparative Analysis; Decision Tree Regression; Fb-Prophet; Holt's Winter Model; Linear Regression; Machine Learning; Stock Price Prediction |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000924286200031 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering |
| Research Area |
Computer Science |
| PDF |
|