Artificial neural network forecasting performance with missing value imputations
This paper presents time series forecasting method in order to achieve high accuracy performance. In this study, the modern time series approach with the presence of missing values problem is developed. The artificial neural networks (ANNs) is used to forecast the future values with the missing valu...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Indian Society for Development and Environment Research
2020
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/87929/1/ABSTRACT.pdf |
| _version_ | 1825952428666650624 |
|---|---|
| author | Abd Rahman, Nur Haizum Lee, Muhammad Hisyam |
| author_facet | Abd Rahman, Nur Haizum Lee, Muhammad Hisyam |
| author_sort | Abd Rahman, Nur Haizum |
| collection | UPM |
| description | This paper presents time series forecasting method in order to achieve high accuracy performance. In this study, the modern time series approach with the presence of missing values problem is developed. The artificial neural networks (ANNs) is used to forecast the future values with the missing value imputations methods used known as average, normal ratio and also the modified method. The results are validated by using mean absolute error (MAE) and root mean square error (RMSE). The result shown that by considering the right method in missing values problems can improved artificial neural network forecast accuracy. It is proven in both MAE and
RMSE measurements as forecast improved from 8.75 to 4.56 and from 10.57 to 5.85 respectively. Thus, this study suggests by understanding the problem in time series data can produce accurate forecast and the correct decision making can be produced. |
| first_indexed | 2024-03-06T10:44:29Z |
| format | Article |
| id | upm.eprints-87929 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:44:29Z |
| publishDate | 2020 |
| publisher | Indian Society for Development and Environment Research |
| record_format | dspace |
| spelling | upm.eprints-879292022-05-24T08:00:50Z http://psasir.upm.edu.my/id/eprint/87929/ Artificial neural network forecasting performance with missing value imputations Abd Rahman, Nur Haizum Lee, Muhammad Hisyam This paper presents time series forecasting method in order to achieve high accuracy performance. In this study, the modern time series approach with the presence of missing values problem is developed. The artificial neural networks (ANNs) is used to forecast the future values with the missing value imputations methods used known as average, normal ratio and also the modified method. The results are validated by using mean absolute error (MAE) and root mean square error (RMSE). The result shown that by considering the right method in missing values problems can improved artificial neural network forecast accuracy. It is proven in both MAE and RMSE measurements as forecast improved from 8.75 to 4.56 and from 10.57 to 5.85 respectively. Thus, this study suggests by understanding the problem in time series data can produce accurate forecast and the correct decision making can be produced. Indian Society for Development and Environment Research 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/87929/1/ABSTRACT.pdf Abd Rahman, Nur Haizum and Lee, Muhammad Hisyam (2020) Artificial neural network forecasting performance with missing value imputations. International Journal of Artificial Intelligence, 9 (1). 33 - 39. ISSN 0974-0635 https://ijai.iaescore.com/index.php/IJAI/article/view/20366 10.11591/ijai.v9.i1.pp33-39 |
| spellingShingle | Abd Rahman, Nur Haizum Lee, Muhammad Hisyam Artificial neural network forecasting performance with missing value imputations |
| title | Artificial neural network forecasting performance with missing
value imputations |
| title_full | Artificial neural network forecasting performance with missing
value imputations |
| title_fullStr | Artificial neural network forecasting performance with missing
value imputations |
| title_full_unstemmed | Artificial neural network forecasting performance with missing
value imputations |
| title_short | Artificial neural network forecasting performance with missing
value imputations |
| title_sort | artificial neural network forecasting performance with missing value imputations |
| url | http://psasir.upm.edu.my/id/eprint/87929/1/ABSTRACT.pdf |
| work_keys_str_mv | AT abdrahmannurhaizum artificialneuralnetworkforecastingperformancewithmissingvalueimputations AT leemuhammadhisyam artificialneuralnetworkforecastingperformancewithmissingvalueimputations |