Predicting the highest and lowest stock price before end of the day

Predicting the highest and lowest stock price before end of the day

In this paper, an ensemble model for forecasting highly complex financial time series is being introduced. To use the Autoregressive Integrated Moving Average (ARIMA) and Random Walk with Drift (RWDRIFT) models to capture the characteristics of highly complex financial time series. Experimental resu...

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Bibliographic Details
Main Author: Choo, Zhi Cheng
Other Authors: Quek Hiok Chai
Format: Final Year Project (FYP)
Language:English
Published: 2014
Subjects:
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Online Access:http://hdl.handle.net/10356/58998

Internet

http://hdl.handle.net/10356/58998

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