Estimation of Iron Ore Price in reference to Major Economic Indices using Artificial Neural Network
| dc.contributor.author | Kim, Yoochan | |
| dc.contributor.supervisor | Apurna Ghosh | en_US |
| dc.contributor.supervisor | Erkan Topal | en_US |
| dc.date.accessioned | 2024-07-31T00:45:46Z | |
| dc.date.available | 2024-07-31T00:45:46Z | |
| dc.date.issued | 2024 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/95589 | |
| dc.description.abstract |
Iron ore is an essential commodity in human civilization where research its prediction method was limited to date. The Granger causality test and VECM proved that there is a bi-directional influence between the iron ore price and the oil, copper, and Australian coal prices. Linear Levenberg-Marquardt method predicted with highest accuracy of 5.92% difference between prediction and actual for 1 month ahead, 9.48% for 2 months ahead, and 11.21% for 3 months ahead respectively. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | Estimation of Iron Ore Price in reference to Major Economic Indices using Artificial Neural Network | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | MPhil | en_US |
| curtin.department | WASM: Minerals, Energy and Chemical Engineering | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Science and Engineering | en_US |
| curtin.contributor.orcid | Kim, Yoochan [0000-0001-7649-3903] | en_US |