On improved fitting using a new probability distribution and artificial neural network: Application
Statistical modeling and forecasting are crucial to understanding the depth of information in data from all sources. For precision purposes, researchers are always in search of ways to improve the quality of modeling and forecasting, whatever the complexity of the situation. To this end, new (probab...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2023-11-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0176715 |
| _version_ | 1797404819505283072 |
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| author | Sanaa Al-Marzouki Afaf Alrashidi Christophe Chesneau Mohammed Elgarhy Rana H. Khashab Suleman Nasiru |
| author_facet | Sanaa Al-Marzouki Afaf Alrashidi Christophe Chesneau Mohammed Elgarhy Rana H. Khashab Suleman Nasiru |
| author_sort | Sanaa Al-Marzouki |
| collection | DOAJ |
| description | Statistical modeling and forecasting are crucial to understanding the depth of information in data from all sources. For precision purposes, researchers are always in search of ways to improve the quality of modeling and forecasting, whatever the complexity of the situation. To this end, new (probability) distributions and suitable forecasting methods are demanded. The first part of this paper contributes to this direction. Indeed, we introduce a modified version of the flexible Weibull distribution, called the modified flexible Weibull distribution. It is constructed by mixing the flexible Weibull distribution with the exponential T-X scheme. This strategy is winning; the new distribution has a larger panel of functionalities in comparison to those of the classical Weibull distribution, among other things. To check the quality of the fitting of the modified flexible Weibull distribution, two different datasets are analyzed. After analyzing these datasets, it is observed that the modified flexible Weibull distribution has improved fitting power compared to other similar distributions. Apart from this, the conventional time series model, namely, the autoregressive integrated moving average (ARIMA) model, and the modern artificial neural network (ANN) model are considered for forecasting results. Utilizing the two datasets discussed earlier, it was discovered that the ANN model is more effective than the traditional ARIMA model. |
| first_indexed | 2024-03-09T03:00:32Z |
| format | Article |
| id | doaj.art-fde4c6df61874acaa7472a3a60bd38fa |
| institution | Directory Open Access Journal |
| issn | 2158-3226 |
| language | English |
| last_indexed | 2024-03-09T03:00:32Z |
| publishDate | 2023-11-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj.art-fde4c6df61874acaa7472a3a60bd38fa2023-12-04T17:18:29ZengAIP Publishing LLCAIP Advances2158-32262023-11-011311115209115209-1610.1063/5.0176715On improved fitting using a new probability distribution and artificial neural network: ApplicationSanaa Al-Marzouki0Afaf Alrashidi1Christophe Chesneau2Mohammed Elgarhy3Rana H. Khashab4Suleman Nasiru5Statistics Department, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi ArabiaDepartment of Statistics, Faculty of Science, University of Tabuk, Tabuk, Saudi ArabiaDepartment of Mathematics, University of Caen-Normandie, 14000 Caen, FranceDepartment of Basic Sciences, Higher Institute of Administrative Sciences, AlSharkia, Belbeis, EgyptMathematics Department, Faculty of Sciences, Umm Al-Qura University, Makkah, Saudi ArabiaDepartment of Statistics and Actuarial Science, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, GhanaStatistical modeling and forecasting are crucial to understanding the depth of information in data from all sources. For precision purposes, researchers are always in search of ways to improve the quality of modeling and forecasting, whatever the complexity of the situation. To this end, new (probability) distributions and suitable forecasting methods are demanded. The first part of this paper contributes to this direction. Indeed, we introduce a modified version of the flexible Weibull distribution, called the modified flexible Weibull distribution. It is constructed by mixing the flexible Weibull distribution with the exponential T-X scheme. This strategy is winning; the new distribution has a larger panel of functionalities in comparison to those of the classical Weibull distribution, among other things. To check the quality of the fitting of the modified flexible Weibull distribution, two different datasets are analyzed. After analyzing these datasets, it is observed that the modified flexible Weibull distribution has improved fitting power compared to other similar distributions. Apart from this, the conventional time series model, namely, the autoregressive integrated moving average (ARIMA) model, and the modern artificial neural network (ANN) model are considered for forecasting results. Utilizing the two datasets discussed earlier, it was discovered that the ANN model is more effective than the traditional ARIMA model.http://dx.doi.org/10.1063/5.0176715 |
| spellingShingle | Sanaa Al-Marzouki Afaf Alrashidi Christophe Chesneau Mohammed Elgarhy Rana H. Khashab Suleman Nasiru On improved fitting using a new probability distribution and artificial neural network: Application AIP Advances |
| title | On improved fitting using a new probability distribution and artificial neural network: Application |
| title_full | On improved fitting using a new probability distribution and artificial neural network: Application |
| title_fullStr | On improved fitting using a new probability distribution and artificial neural network: Application |
| title_full_unstemmed | On improved fitting using a new probability distribution and artificial neural network: Application |
| title_short | On improved fitting using a new probability distribution and artificial neural network: Application |
| title_sort | on improved fitting using a new probability distribution and artificial neural network application |
| url | http://dx.doi.org/10.1063/5.0176715 |
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