Abstract
Nowadays every one interest to put their investments into the Stock Market. The price of the stock is an important indicator for the companies economic growth evaluation. Some factors can be affected to the growth of the Share Market like Sudden Disasters, Political Issues, etc. To predict these market challenges based on the Time series analysis, stock price variations also depend on previous stock prices. Stock Feature measurement techniques are applied in stock estimating factors. Previous methodologies typically utilize a solitary component choice strategy, which may disregard some significant presumptions about the basic relapse work connecting the information and yield factors. Here, we implement the features of selected stock price values used by many feature selection methods to generate an exact feature part and then use a random forest model to predict upcoming stock price movements. Because of its incredible learning capacity for settling the nonlinear time arrangement expectation issues, artificial intelligence has been applied to this examination territory. Learning-based techniques for stock value forecast are extremely famous and plenty of upgraded systems have been utilized to improve the presentation of the learning-based indicators.
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Srinu Vasarao, P., Chakkaravarthy, M. (2022). Time Series Analysis Using Random Forest for Predicting Stock Variances Efficiency. In: Reddy, V.S., Prasad, V.K., Mallikarjuna Rao, D.N., Satapathy, S.C. (eds) Intelligent Systems and Sustainable Computing. Smart Innovation, Systems and Technologies, vol 289. Springer, Singapore. https://doi.org/10.1007/978-981-19-0011-2_6
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