| Autor: |
Dr. Imtiyaz Khan, Mohd Zubair Uddin, Dr. M Upendra Kumar |
| Rok vydání: |
2023 |
| Zdroj: |
International Journal of Multidisciplinary Research and Growth Evaluation. 4:494-499 |
| ISSN: |
2582-7138 |
| DOI: |
10.54660/.ijmrge.2023.4.2.494-499 |
| Popis: |
This study evaluates the use of Linear regression & Long Short-Term Memory Model for stock price prediction. The linear regression model is trained on historical stock data and used to predict future stock prices. The results show that linear regression can be an effective tool for stock price prediction when the stock market follows a predictable trend. However, the model is limited in its ability to capture complex relationships in the data and may not perform well in volatile or unpredictable stock markets. The LSTM model is trained on sequential stock data and used to predict future stock prices. The results show that LSTM is well-suited for stock price prediction due to its ability to capture long-term dependencies in the data and handle the volatility and randomness of stock prices. However, the model requires a large amount of data and can be computationally expensive to train. Despite these limitations, the results of this study demonstrate that LSTM is a promising tool for stock price prediction. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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