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- W4223551936 abstract "Deep learning has become extremely popular for predicting stock market prices and trends in recent times. Deep learning models require an enormous amount of data for training. As a result, deep learning models may not be a good fit for predicting the price trends of stocks with limited historical data. In this paper, the Gaussian Process Regression approach is used to predict stock market trends. Two stocks/indices are considered in this work, namely State Bank of India (SBI) and Nasdaq. Two sample periods for both the stocks: the first period is January 2008 – December 2019 for predicting the stock price for 2020, while the second period is January 2008 – November 2020 for predicting the stock price for the second half of the year 2021 have been used. Several feature engineering approaches are utilized to pre-process the stocks' input data. The Gaussian process Regression model has shown a decent performance in the experiments." @default.
- W4223551936 created "2022-04-15" @default.
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- W4223551936 date "2022-03-29" @default.
- W4223551936 modified "2023-09-23" @default.
- W4223551936 title "Stock Prediction using Gaussian Process Regression" @default.
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- W4223551936 doi "https://doi.org/10.1109/iccmc53470.2022.9754114" @default.
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