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- W4383670024 abstract "In the era of big data, various methods were utilized for forecasting the stock market established on standard time series data like real numbers, fuzzy time series data, and fuzzy sets design. Controlling semantic value data so that accurate values prediction can be generated is done by implementing fuzzy time series data in the stock market forecasting. The models are largely implemented for predicting the dynamic and non-linear datasets in various fields like the stock market and cryptocurrency market. Different modern and smart techniques are being used nowadays in forecasting stock-related data, such as soft computing techniques, back-propagation techniques, Neural Network (NN) techniques, and various other techniques. Artificial Intelligence (AI) methods like NN were developed to forecast a stock’s price. Various models were developed which used the feed-forward (FF) NN technique for forecasting the stock market trend and checking different non-parametric and parametric models for predicting the stock market gains. Various soft computing techniques were used for proposing models that dispense with AI for presenting the judgement with the help of loss and profit criteria. The main purpose of the paper is to present a comprehensive review of several prediction techniques utilized for stock market price and return forecasting. This survey contains different forecasting techniques used in stock market forecasting. Also, the paper shows the comparisons between different models." @default.
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- W4383670024 date "2023-01-01" @default.
- W4383670024 modified "2023-09-25" @default.
- W4383670024 title "Machine Learning-Based Stock Market Prediction" @default.
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- W4383670024 doi "https://doi.org/10.1007/978-981-99-0483-9_6" @default.
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