Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock Prices

Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock Prices

Authors

  • Nor Syazwina Binti Mohd Hanafiah Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nor Hayati Binti Shafii Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nur Fatihah Binti Fauzi Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Diana Sirmayunie Mohd Nasir Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nor Azriani Mohamad Nor Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus

DOI:

https://doi.org/10.24191/jcrinn.v7i2.332

Keywords:

Autoregressive Integrated Moving Average, ARIMA, Fuzzy Time Series, Stock Price Forecasting, Share Price Forecasting

Abstract

The stock market has always been a contentious topic in society, and it is a place where economic standards are established. The stock market is incredibly unpredictable and turbulent. This means that the shares may fluctuate for reasons that are sometimes difficult to understand.  Due to this uncertainty, many investors believe the stock market as a risky investment.  Therefore, having an accurate picture of future market environment is crucial to minimising losses. Forecasting is a technique of predicting the future based on the outcome of the previous data.  There are a wide range of forecasting algorithms, however, this study only focuses on these two techniques: Auto Regressive Moving Average (ARIMA) model and Fuzzy Time Series (FTS) Model. The goal of this study is to evaluate and compare the effectiveness of the ARIMA model and the FTS model in predicting sample data of stock prices of Top Glove Corporation Berhad since this company is the largest glove supplier in the world and plays a significant role in the Covid-19 global pandemic crisis. The error measures that were taken into consideration consist of Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). These measurements were computed numerically and graphically using a statistical programme called EViews.  The outcome shows that the ARIMA model performs better than the FTS model in terms of forecasting accuracy and provides the lowest values of MAPE, MSE, and RMSE, which are 10.58757, 0.926354, and 0.962473, respectively.

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References

Alzahrani, S. I., Aljamaan, I. A., Al-Fakih, E. A. (2020). Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions. Journal of Infection and Public Health, 13(7), 914 -919.

Csiszar, J. (2020, November 2). What Makes Stock Prices Go Up or Down? Here Are the Reasons. https://www.gobankingrates.com/investing/stocks/what-makes-a-stock-go-up/amp/

Devi, M., Kumar, J., Malik, D., & Mishra, P. (2021). Forecasting of wheat production in Haryana using hybrid time series model. Journal of Agriculture and Food Research, 5, 100175.

Hwang, H., & Oh, J. (2010). Fuzzy models for predicting time series stock price index. International Journal of Control, Automation and Systems, 8(3), 702–706.

Jilani, T. A., & Burney, S. M. A. (2008). A refined fuzzy time series model for stock market forecasting. Physica A: Statistical Mechanics and Its Applications, 387(12), 2857–2862.

Kumar Meher, B., Thonse Hawaldar, I., Spulbar, C., & Birau, R. (2021). Forecasting stock market prices using mixed ARIMA model: a case study of Indian pharmaceutical companies. Investment Management and Financial Innovations, 18(1), 42–54.

Lazim, M. A. (2005). Introductory Business Forecasting (3rd ed.). Amsterdam University Press.

Lee, M. H., & Suhartono. (2012). A weighted fuzzy time series model for forecasting seasonal data. Journal of Quality Measurement and Analysis (JQMA), 8(1), 85–95.

Phan, T.T.H., Nguyen, X. H. (2020). Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river. Journal of Advances in Water Resources, 142(103656).

Sahai, A. K., Rath, N., Sood, V., Singh, M. P. (2020). ARIMA modelling & forecasting of COVID-19 in top five affected countries. Diabetes Metab Syndr, 14(5), 1419-1427

Zadeh, L. A. (1965). Fuzzy sets. Journal of Information and Control, 8(3), 338 – 353.

Zhang, X., Wu, Q., & Zhang, J. (2010). Crude oil price forecasting using fuzzy time series. 2010 3rd International Symposium on Knowledge Acquisition and Modeling, KAM 2010, 213–216.

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Published

2022-09-01

How to Cite

Nor Syazwina Binti Mohd Hanafiah, Shafii, N. H. B., Nur Fatihah Binti Fauzi, Diana Sirmayunie Mohd Nasir, & Nor Azriani Mohamad Nor. (2022). Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock Prices. Journal of Computing Research and Innovation, 7(2), 366–378. https://doi.org/10.24191/jcrinn.v7i2.332

Issue

Section

General Computing

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