Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock Prices
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 unce...
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Format: | Article |
Language: | English |
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Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis
2022-09-01
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Series: | Journal of Computing Research and Innovation |
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Online Access: | https://jcrinn.com/index.php/jcrinn/article/view/332 |
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author | Nor Syazwina Binti Mohd Hanafiah Nor Hayati Binti Shafii Nur Fatihah Binti Fauzi Diana Sirmayunie Mohd Nasir Nor Azriani Mohamad Nor |
author_facet | Nor Syazwina Binti Mohd Hanafiah Nor Hayati Binti Shafii Nur Fatihah Binti Fauzi Diana Sirmayunie Mohd Nasir Nor Azriani Mohamad Nor |
author_sort | Nor Syazwina Binti Mohd Hanafiah |
collection | DOAJ |
description |
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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first_indexed | 2024-03-11T13:37:00Z |
format | Article |
id | doaj.art-1083121c2b3542d29dc1cc0f3ed478a2 |
institution | Directory Open Access Journal |
issn | 2600-8793 |
language | English |
last_indexed | 2024-03-11T13:37:00Z |
publishDate | 2022-09-01 |
publisher | Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis |
record_format | Article |
series | Journal of Computing Research and Innovation |
spelling | doaj.art-1083121c2b3542d29dc1cc0f3ed478a22023-11-02T15:09:49ZengFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA PerlisJournal of Computing Research and Innovation2600-87932022-09-0172Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock PricesNor Syazwina Binti Mohd Hanafiah0Nor Hayati Binti Shafii1Nur Fatihah Binti Fauzi2Diana Sirmayunie Mohd Nasir3Nor Azriani Mohamad Nor4Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau CampusFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau CampusFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau CampusFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau CampusFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus 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. https://jcrinn.com/index.php/jcrinn/article/view/332Autoregressive Integrated Moving AverageARIMAFuzzy Time SeriesStock Price ForecastingShare Price Forecasting |
spellingShingle | Nor Syazwina Binti Mohd Hanafiah Nor Hayati Binti Shafii Nur Fatihah Binti Fauzi Diana Sirmayunie Mohd Nasir Nor Azriani Mohamad Nor Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock Prices Journal of Computing Research and Innovation Autoregressive Integrated Moving Average ARIMA Fuzzy Time Series Stock Price Forecasting Share Price Forecasting |
title | Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock Prices |
title_full | Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock Prices |
title_fullStr | Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock Prices |
title_full_unstemmed | Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock Prices |
title_short | Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock Prices |
title_sort | comparison of fuzzy time series and arima model for predicting stock prices |
topic | Autoregressive Integrated Moving Average ARIMA Fuzzy Time Series Stock Price Forecasting Share Price Forecasting |
url | https://jcrinn.com/index.php/jcrinn/article/view/332 |
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