Stock Prediction using Long Short-Term Memory, Support Vector Regression and Linear Regression Algorithms
Prediction is most important for stock market not only for traders but also for computer engineers who analyses stock data. We can perform this prediction by two ways one is using historical stock data and other by analyzing by information gathered from social media. It is based on model/pattern use...
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| Veröffentlicht in: | International journal for research in applied science and engineering technology Jg. 9; H. VII; S. 3632 - 3638 |
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| 1. Verfasser: | |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
31.07.2021
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| ISSN: | 2321-9653, 2321-9653 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Prediction is most important for stock market not only for traders but also for computer engineers who analyses stock data. We can perform this prediction by two ways one is using historical stock data and other by analyzing by information gathered from social media. It is based on model/pattern used to predict stock dataset, there are so many models are available for predicting stocks, simply model is algorithm that’s from machine learning and deep learning. In the data set the two main parameters open and close value are used for stock prediction mostly but we can also predict by its volume too. So that data is preprocessed before it is used for prediction. In this paper we used various algorithm like linear regression, support vector regression and long short-term memory for better accuracy and to compare how it different from other algorithm and for predicting future stock. |
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| ISSN: | 2321-9653 2321-9653 |
| DOI: | 10.22214/ijraset.2021.37183 |