Abstract
The stock market is a sector that is very tempting to various kinds of people, and almost every alternate households invest their money to get better returns. Data plays a vital role in these predictions, and new tools like machine learning techniques provide additional support, which gives a range of data for forecasting values. Nowadays, different models are available to predict the outcomes of the stock market. In this paper, two models are used and analysed for predicting the stocks: regression and FB prophet. We have used a dataset of Infosys stock for over five years, and with this data, forecasting is done in the upcoming months. We have referred to various models for their working and outcomes and have chosen regression and FB prophet models for predicting stock prices and analysing their effectiveness using error % and Mean Absolute Percentage Error (MAPE) analysis. Thus, the outcomes show us that FB prophet is more accurate than the regression model in which the regression shows the error of about 14–16%, and in Fb prophet, the values were about 3–6%.
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Paygude, P., Tiwari, A., Goel, B., Kabra, A. (2023). Comparative Analysis of Stock Prices by Regression Analysis and FB Prophet Models. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-6004-8_24
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DOI: https://doi.org/10.1007/978-981-19-6004-8_24
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