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Title: Stock market price forecasting with machine learning methods
Authors: Zhu, Huilin
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2017
Abstract: With the development of economy and the change of people's investment consciousness, stock investment has been an important part in daily life, and stock forecasting has also been the focus of investors and financial researchers. Since the income and risk of stock investment is directly proportional, how to establish a relatively high speed and high accurate stock market forecasting model is significant and also has practical value for financial investors. My research mainly introduces the development of time series forecasting, and also gives a review of existing algorithms. In this dissertation, I mainly focus on four forecasting methods namely ANN, SVM, RVFL and RF, along with the experiments we have set up for evaluation. Moreover, a method for signal decomposition called empirical mode decomposition is used to improve the accuracy, along with five datasets that are utilized to test and verify the effectiveness of the proposed method.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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