Azlan, Ammar and Yusof, Yuhanis and Mohamad Mohsin, Mohamad Farhan (2019) Determining the impact of window length on time series forecasting using deep learning. International Journal of Advanced Computer Research, 9 (44). pp. 260-267. ISSN 22497277

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Abstract

Time series forecasting is a method of predicting the future based on previous observations. It depends on the values of the same variable, but at different time periods. To date, various models have been used in stock market time series forecasting, in particular using deep learning models. However, existing implementations of the models did not determine the suitable number of previous observations, that is the window length. Hence, this study investigates the impact of window length of long short-term memory model in forecasting stock market price. The forecasting is performed on S&P500 daily closing price data set. A different window length of 25-day, 50-day, and 100-day were tested on the same model and data set. The result of the experiment shows that different window length produced different forecasting accuracy. In the employed dataset, it is best to utilize 100 as the window length in forecasting the stock market price. Such a finding indicates the importance of determining the suitable window length for the problem in-hand as there is no One-Size-Fits-All model in time series forecasting.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Long short-term memory (LSTM), Time series forecasting, Window length.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	10 Mar 2020 08:57
Last Modified:	10 Mar 2020 08:57
URI:	https://repo.uum.edu.my/id/eprint/26893

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