Modelling Time Series and Forecasting for Computer Forms (Malaysia) Bhd, from Bursa Malaysia

Authors

  • Loshana Parthipan UTHM

Keywords:

Forecasting, ARIMA, GARCH

Abstract

In this study, we will examine the stock price predicting of Computer Forms (Malaysia) Bhd from 2016 to 2021 and select the best model. However, visualizing a time series requires a lot of apparent statistical data collected at regular intervals. The use of autoregressive integrated moving average (ARIMA) and Generalized autoregressive conditional heteroscedasticity (GARCH) models rather than determining directly produces more legitimate and solid results. There are three ARIMA models: (0,1,1), (1,1,0) and  (2,1,0). The best model (0,1,1) has the lowest MSE and the highest Ljung-Box p-value. Comparing the GARCH (1,1) models, the student t's model is picked as the best. MAE, RMSE, and MAPE are used to assess forecasting accuracy. It was concluded the GARCH model is the best way to forecast Computer Forms Bhd, Bursa Malaysia

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Published

29-11-2022

How to Cite

Parthipan, L. (2022). Modelling Time Series and Forecasting for Computer Forms (Malaysia) Bhd, from Bursa Malaysia. Enhanced Knowledge in Sciences and Technology, 2(2), 001–010. Retrieved from https://publisher.uthm.edu.my/periodicals/index.php/ekst/article/view/5314

Issue

Section

Statistics