Prediction of share market price using LSTM and compare accuracy with linear regression algorithm
AIP Conference Proceedings, ISSN: 1551-7616, Vol: 2871, Issue: 1
2024
- 1Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures1
- Readers1
Conference Paper Description
Improving the accuracy and precision of stock market share price predictions using Innovative Long Short-Term Memory compared to Linear Regression (LR) is the major purpose of this research study. This paper's dataset demonstrates the approach's efficacy using the publicly accessible dataset from the National Stock Exchange (NSE). For this stock market prediction, we used a sample size of 280 (140 in Group 1 and 140 in Group 2), and we used G-power 0.8 with alpha and beta values of 0.05 and 0.2, respectively, and a confidence interval of 95%. With a number of samples (N=10), LR achieves better accuracy and precision in predicting stock prices on the stock market. The LR classifier's accuracy rate is 86.63%, while the Novel Long Short-Term Memory classifier's rate is 93.94%. A relevance score of p<0.05, or p=0.0271, indicates that the research is valid. In conclusion, when it comes to predicting stock prices on the stock market, Novel Long Short-Term Memory outperforms LR in terms of accuracy and precision.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know