Bhakiya*, S. and Akila*, Dr.A. and Parameswari, Dr. R. (2019) Pythagoras Expectation based Mining Technique for Stock Market Divination. International Journal of Recent Technology and Engineering (IJRTE), 8 (3). pp. 5362-5365. ISSN 22773878
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Abstract
Pythagoras Expectation based Mining Technique for Stock Market Divination Department of Computer Science, Vels Institute of Science, Technology & Advanced Studies, Chennai, India. S. Bhakiya* Dr.A. Akila* Department of Computer Science, Vels Institute of Science, Technology & Advanced Studies, Chennai, India. Dr. R. Parameswari Department of Computer Science , Vels Institute of Science, Technology and Advanced Studies, Chennai, India.
Analysis of stocks will be helpful for the new investors to invest in the stock market depending on the different factors of the application. Stock market checks the daily tasks for manipulation of Sensex, sharestrading and stock market. The exchange gives way for a well-organized and open market for trading in fair, debt instruments and derivatives. Since the last decade, there is an increased need for improving the accuracy of forecasting models in various domains. This paper uses Pythagoras Expectation for Stock Market Prediction. There is a real urge to find the appropriate stock investment which would have a good return. The aim of this article is to predicate the prediction of financial movements in stock market. The proposed work is experimented using the dataset fetched from yahoo finance and the results were verified and found to be significant using ARIMA model.
09 30 2019 5362 5365 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.C6089.098319 https://www.ijrte.org/portfolio-item/C6089098319/ https://www.ijrte.org/wp-content/uploads/papers/v8i3/C6089098319.pdf
Item Type: | Article |
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Subjects: | Computer Science Engineering > Data Mining |
Divisions: | Computer Science |
Depositing User: | Mr IR Admin |
Date Deposited: | 10 Oct 2024 12:04 |
Last Modified: | 10 Oct 2024 12:04 |
URI: | https://ir.vistas.ac.in/id/eprint/9703 |