A comparison of multiple imputation technique with linear interpolation method for time series data
| dc.contributor.author | Acheampong, Emmanuel | |
| dc.contributor.author | ||
| dc.date.accessioned | 2021-06-28T11:31:37Z | |
| dc.date.accessioned | 2023-04-19T04:04:15Z | |
| dc.date.available | 2021-06-28T11:31:37Z | |
| dc.date.available | 2023-04-19T04:04:15Z | |
| dc.date.issued | SEPTEMBER, 2019 | |
| dc.description | A thesis submitted to the Department of Mathematics, Kwame Nkrumah University of Science and Technology, in partial fufillment of the requirement for the degree of Msc. Applied Statistics. | en_US |
| dc.description.abstract | This thesis evaluates the performances of Multiple Imputation Technique (MIT) and Linear Interpolation methods for the estimation of missing values in a time series data (CO2 emissions data under the Fuel combustion sub-category of the Energy sector. Under this sub-category, data of two codes namely; i) Energy industries and ii) Manufacturing Industries and Construction were used). The performances of both methods were then compared using two notable indicators; the Mean Absolute Error (MAE) and the Mean-Square Error (RMSE). This thesis highlights some advantages and limitations of each method compared with the other, thereby providing suggestions on which method to be used under prevailing conditions. | en_US |
| dc.description.sponsorship | KNUST | en_US |
| dc.identifier.uri | https://ir.knust.edu.gh/handle/123456789/14161 | |
| dc.language.iso | en_US | en_US |
| dc.subject | MIT | en_US |
| dc.subject | Linear Interpolation methods | en_US |
| dc.title | A comparison of multiple imputation technique with linear interpolation method for time series data | en_US |
| dc.type | Thesis | en_US |
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