Abstract
This paper presentsa comparison of three different non-lineartime series modelling approaches:NARMAX (Non-linearAutoregressive Moving Average with Exogenous Inputs), Beta-t-EGARCH (Beta t Exponential Generalized Autoregressive Conditional Heteroscedasticity), andRadial Basis Function Neural Networks (RBFNN) applied to weekly stock market index data.We will explain three types of models and compare their compositions and structures. Then, we will show which model gives better predictions.To study series data, the comparison involvedanalysingthe structure of the model and its errors in various time series models and summarising their findings. We divide the data into two parts: training data to structure the time series and testing. The training data teststhe model's predictions. Then, we can analyse the model with the errors and the best deterrence predictions. After selecting the NARMAX and Beta-t-EGARCH models, we test them with specific criteria. The best choice is findingthe model withthe lowest average errors.For this study, we analysedthe weekly average closing of the Aramco 2222 index from 15 December 2019 to 16July 2023and made187 observations
Recommended Citation
Abdullah, Hiba H.; Khalaf, Nihad S.; and Noori, Nooruldeen A.
(2024)
"Comparison of non-linear time series models (Beta-t-EGARCH and NARMAX models) with Radial Basis Function Neural Network usingReal Data,"
Iraqi Journal for Computer Science and Mathematics: Vol. 5:
Iss.
3, Article 38.
DOI: https://doi.org/10.52866/ijcsm.2024.05.03.003
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol5/iss3/38
