Abstract
Fundamentally, stock markets stand for an undeviating, nonparametric system accompanied with undetermined disturbances due to which the accurate prediction of the stock price becomes challenging. Stock prediction is achievable with the help of technology assisted practises. The objective of this review paper is to compare machine learning algorithms namely Support Vector Regression (SVR), Improved Levenberg Marquardt Algorithm (LMA) Self-adapting Variant PSO-Elman Neural Network (PSO) and Linear Regression (LR) so as to have a precise price trend.
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Potdar, J., Mathew, R. (2020). Machine Learning Algorithms in Stock Market Prediction. In: Pandian, A.P., Senjyu, T., Islam, S.M.S., Wang, H. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018). ICCBI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-24643-3_23
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DOI: https://doi.org/10.1007/978-3-030-24643-3_23
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