Developing a hybrid hidden MARKOV model using fusion of ARMA model and artificial neural network for crude oil price forecasting

Isah, Nuhu (2020) Developing a hybrid hidden MARKOV model using fusion of ARMA model and artificial neural network for crude oil price forecasting. Doctoral thesis, Universiti Tun Hussein Onn Malaysia.

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Abstract

Crude oil price forecasting is an important component of sustainable development of many countries as crude oil is an unavoidable product that exist on earth. Crude oil price forecasting plays a very vital role in economic development of many countries in the world today. Any fluctuation in crude oil price tremendously affects many economies in terms of budget and expenditure. In view of this, it is of great concern by economists and financial analysts to forecast such a vital commodity. However, Hidden Markov Model, ARMA Model and Artificial Neural Network has many drawbacks in forecasting such as linear limitations of ARMA model which is in contrast to the financial time series which are often nonlinear, ANN is very weak in terms of out-sample forecast and it has very tedious process of implementation, HMM is very weak in an in-sample forecast and has issue of a large number of unstructured parameters. In view of this drawbacks of these three models (ANN, ARMA and HMM), we developed an efficient Hybrid Hidden Markov Model using fusion of ARMA Model and Artificial Neural Network for crude oil price forecasting, MATLAB was employed to develop the four models (Hybrid HMM, HMM, ARMA and ANN). The models were evaluated using three different evaluation techniques which are Mean Absolute Percentage Error (MAPE), Absolute Error (AE) and Root Mean Square Error (RMSE). The findings showed that Hybrid Hidden Markov Model was found to provide more accurate crude oil price forecast than the other three models in which. The results of this study indicate that Hybrid Hidden Markov Model using fusion of ARMA and ANN is a potentially promising model for crude oil price forecasting.

Item Type: Thesis (Doctoral)
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions: Faculty of Technology Management and Business > Department of Technology Management
Depositing User: Mrs. Sabarina Che Mat
Date Deposited: 09 Sep 2021 05:59
Last Modified: 09 Sep 2021 05:59
URI: http://eprints.uthm.edu.my/id/eprint/924

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