Estimation and application of best ARIMA model for forecasting the uranium price.
| dc.contributor.author | Amangeldi, Medeu | |
| dc.date.accessioned | 2018-05-28T08:49:51Z | |
| dc.date.available | 2018-05-28T08:49:51Z | |
| dc.date.issued | 2018-05-13 | |
| dc.description.abstract | This paper presents the application of an iterative approach for prediction of uranium price by model identification, parameter estimation and diagnostic checking which are designed by Box and Jenkins. In particular, the autoregressive integrated moving average model is used to predict the future values of monthly uranium price. As the analysis of structural dependence in observations is one of the key features of time series analysis, the past values, which were taken as monthly values from January 2000 to June 2017, are used for forecasting. As a result, ARIMA (2,1,0) became one that met all the criteria and predicted the increase of uranium price over time within 95% confidence. | en_US |
| dc.identifier.citation | Amangeldi, Medeu. (2018) Estimation and application of best ARIMA model for forecasting the uranium price. Nazarbayev University School of Science and Technology | en_US |
| dc.identifier.uri | http://nur.nu.edu.kz/handle/123456789/3198 | |
| dc.language.iso | en | en_US |
| dc.publisher | Nazarbayev University School of Science and Technology | en_US |
| dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
| dc.subject | Autoregressive Integrated Moving Average (ARIMA) | en_US |
| dc.subject | uranium price | en_US |
| dc.title | Estimation and application of best ARIMA model for forecasting the uranium price. | en_US |
| dc.type | Capstone Project | en_US |
| workflow.import.source | science |