International Journal For Multidisciplinary Research
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Volume 6 Issue 6
November-December 2024
Indexing Partners
Stock Price Prediction using Facebook Prophet
Author(s) | Dole Mangesh, Vishal Sonawane, Ansari Ehtesham, Farooqui Sharique, Algat Y.S. |
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Country | India |
Abstract | Our time is the era of machine learning. To make work easier, every profession is implementing machine learning techniques. It is difficult to predict the stock market since it needs in-depth knowledge of how to ignore news events, assess past data, and determine how news events affect stock price trends. The difficulty is made more difficult by how erratic stock prices are. A fair price in the stock market is the result of the prophecy of equalizing sales. The goal of stock price prediction is to forecast the value of a company's financial shares in the future. The application of machine learning, which produces forecasts based on current stock market indicators by training in their prior values, is the most recent development in stock market forecasting technology. Different models are used by machine learning to produce predictions that are simpler and more accurate. In this instance, Facebook Prophet is used to forecast future stock market ratings and examine how those prices will compare to those of earlier stock markets. This effort dedicates itself to analyzing the sales rate with state-of-the-art design and consideration of preconceived conceptions and preceding data processing procedures |
Keywords | Stocks Prediction, Facebook Prophet, Arima, Stock Price Correlation |
Field | Computer > Artificial Intelligence / Simulation / Virtual Reality |
Published In | Volume 5, Issue 2, March-April 2023 |
Published On | 2023-04-27 |
Cite This | Stock Price Prediction using Facebook Prophet - Dole Mangesh, Vishal Sonawane, Ansari Ehtesham, Farooqui Sharique, Algat Y.S. - IJFMR Volume 5, Issue 2, March-April 2023. DOI 10.36948/ijfmr.2023.v05i02.2681 |
DOI | https://doi.org/10.36948/ijfmr.2023.v05i02.2681 |
Short DOI | https://doi.org/gr7hjk |
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E-ISSN 2582-2160
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