Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price
Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting t...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/7/2364 |
| _version_ | 1797571932055404544 |
|---|---|
| author | Diyuan Li Mohammad Reza Moghaddam Masoud Monjezi Danial Jahed Armaghani Amirhossein Mehrdanesh |
| author_facet | Diyuan Li Mohammad Reza Moghaddam Masoud Monjezi Danial Jahed Armaghani Amirhossein Mehrdanesh |
| author_sort | Diyuan Li |
| collection | DOAJ |
| description | Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques. |
| first_indexed | 2024-03-10T20:47:37Z |
| format | Article |
| id | doaj.art-24096915582d4fa79c4bf797d45ee38c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:47:37Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-24096915582d4fa79c4bf797d45ee38c2023-11-19T20:09:48ZengMDPI AGApplied Sciences2076-34172020-03-01107236410.3390/app10072364Development of a Group Method of Data Handling Technique to Forecast Iron Ore PriceDiyuan Li0Mohammad Reza Moghaddam1Masoud Monjezi2Danial Jahed Armaghani3Amirhossein Mehrdanesh4School of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaFaculty of Engineering, Islamic Azad University, South Tehran Branch, Tehran 1584743311, IranDepartment of Mining, Faculty of Engineering, Tarbiat Modares University, Tehran 14115–143, IranInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamDepartment of Mining, Faculty of Engineering, Tarbiat Modares University, Tehran 14115–143, IranIron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques.https://www.mdpi.com/2076-3417/10/7/2364iron ore price predictiongroup method of data handlingsupport vector regressionclassification and regression treeautoregressive integrated moving average |
| spellingShingle | Diyuan Li Mohammad Reza Moghaddam Masoud Monjezi Danial Jahed Armaghani Amirhossein Mehrdanesh Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price Applied Sciences iron ore price prediction group method of data handling support vector regression classification and regression tree autoregressive integrated moving average |
| title | Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price |
| title_full | Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price |
| title_fullStr | Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price |
| title_full_unstemmed | Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price |
| title_short | Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price |
| title_sort | development of a group method of data handling technique to forecast iron ore price |
| topic | iron ore price prediction group method of data handling support vector regression classification and regression tree autoregressive integrated moving average |
| url | https://www.mdpi.com/2076-3417/10/7/2364 |
| work_keys_str_mv | AT diyuanli developmentofagroupmethodofdatahandlingtechniquetoforecastironoreprice AT mohammadrezamoghaddam developmentofagroupmethodofdatahandlingtechniquetoforecastironoreprice AT masoudmonjezi developmentofagroupmethodofdatahandlingtechniquetoforecastironoreprice AT danialjahedarmaghani developmentofagroupmethodofdatahandlingtechniquetoforecastironoreprice AT amirhosseinmehrdanesh developmentofagroupmethodofdatahandlingtechniquetoforecastironoreprice |