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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Live STOCK PRICE PREDICTION USING LSTM |
| Authors Name: | Aditya Raj Gupta , Anupam Joshi , Agraj Sharma , Sandeep Kumar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_193430
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| Published Paper Id: | IJNRD2305203 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | The economy's most important component continues to be the stock market, and investors still have a great deal of difficulty in properly forecasting stock values. Simple models fall short of generating accurate forecasts, making it difficult for investors in the financial markets to make well-informed choices. Deep learning, a subfield of artificial intelligence that enables computers to carry out activities requiring human intellect, has gained impetus in scientific study as a result of the quick development of technology. This article suggests leveraging real-time data and deep learning techniques like recurrent neural networks (RNN) and long short-term memories (LSTM) to create a precise and accurate stock price prediction model. The suggested model will anticipate future stock prices using real-time data, past stock prices, and other pertinent variables. Prediction accuracy is anticipated to increase using LSTM, a form of RNN that can represent long-term relationships in time-series data. The suggested model will continually learn and adjust to fresh market tendencies, guaranteeing that it offers accurate and current forecasts. In conclusion, this paper tries to increase the accuracy of stock price prediction by utilising deep learning methods and real-time data. Investors in the financial markets may find value in the suggested model's insights, which will allow them to make wise investment choices in real time. |
| Keywords: | Live stock price prediction, LSTM, Recurrent Neural Network, Time-series data, Stock market forecasting, Real-time datal, Historical stock prices, Trading volume, News sentiment, Technical indicators, Mean Absolute Error, MAE, Root Mean Squared Error, RMSE, Financial forecasting, Investment, Machine learning , Deep learning. |
| Cite Article: | "Live STOCK PRICE PREDICTION USING LSTM", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.c15-c20, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305203.pdf |
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2305203 Registration ID: 193430 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: c15-c20 Country: GREATER NOIDA, Uttar Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305203 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305203 |
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