Modeling and forecasting of the high-dimensional time series data with functional data analysis and machine learning approaches
Temperature impacts every part of the world. Meteorological analysis and weather forecasting play a crucial role in sustainable development by helping reduce the damage caused by extreme weather events. A key indicator of climate change is the change in surface temperature. This research focuses on...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2025.1600278/full |
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| author | Yousuf Alkhezi Hajar M. Alkhezi Ahmad Shafee |
| author_facet | Yousuf Alkhezi Hajar M. Alkhezi Ahmad Shafee |
| author_sort | Yousuf Alkhezi |
| collection | DOAJ |
| description | Temperature impacts every part of the world. Meteorological analysis and weather forecasting play a crucial role in sustainable development by helping reduce the damage caused by extreme weather events. A key indicator of climate change is the change in surface temperature. This research focuses on developing and testing a lightweight and innovative weather prediction system that uses local weather stations and advanced functional time series (FTS) techniques to forecast air temperature (AT). The system is built on the latest functional autoregressive model of order one [FAR(1)]. Our results show that the proposed model provides more accurate forecasts than machine learning techniques. Additionally, we demonstrate that our model outperforms several benchmark methods in predicting AT. |
| format | Article |
| id | doaj-art-3969a5f94d064aeab3e47d1bfe886203 |
| institution | Kabale University |
| issn | 2297-4687 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj-art-3969a5f94d064aeab3e47d1bfe8862032025-08-20T04:00:27ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872025-08-011110.3389/fams.2025.16002781600278Modeling and forecasting of the high-dimensional time series data with functional data analysis and machine learning approachesYousuf Alkhezi0Hajar M. Alkhezi1Ahmad Shafee2Mathematics Department, College of Basic Education, Public Authority for Applied Education and Training (PAAET), Kuwait City, KuwaitDepartment of Statistics and Operations Research, Faculty of Science, Kuwait University, Safat, KuwaitLaboratory Technology Department, College of Technological Studies, PAAET, Kuwait City, KuwaitTemperature impacts every part of the world. Meteorological analysis and weather forecasting play a crucial role in sustainable development by helping reduce the damage caused by extreme weather events. A key indicator of climate change is the change in surface temperature. This research focuses on developing and testing a lightweight and innovative weather prediction system that uses local weather stations and advanced functional time series (FTS) techniques to forecast air temperature (AT). The system is built on the latest functional autoregressive model of order one [FAR(1)]. Our results show that the proposed model provides more accurate forecasts than machine learning techniques. Additionally, we demonstrate that our model outperforms several benchmark methods in predicting AT.https://www.frontiersin.org/articles/10.3389/fams.2025.1600278/fullfunctional autoregressivefunctional time seriesartificial neural networkneural network autoregressivesupport vector machine |
| spellingShingle | Yousuf Alkhezi Hajar M. Alkhezi Ahmad Shafee Modeling and forecasting of the high-dimensional time series data with functional data analysis and machine learning approaches Frontiers in Applied Mathematics and Statistics functional autoregressive functional time series artificial neural network neural network autoregressive support vector machine |
| title | Modeling and forecasting of the high-dimensional time series data with functional data analysis and machine learning approaches |
| title_full | Modeling and forecasting of the high-dimensional time series data with functional data analysis and machine learning approaches |
| title_fullStr | Modeling and forecasting of the high-dimensional time series data with functional data analysis and machine learning approaches |
| title_full_unstemmed | Modeling and forecasting of the high-dimensional time series data with functional data analysis and machine learning approaches |
| title_short | Modeling and forecasting of the high-dimensional time series data with functional data analysis and machine learning approaches |
| title_sort | modeling and forecasting of the high dimensional time series data with functional data analysis and machine learning approaches |
| topic | functional autoregressive functional time series artificial neural network neural network autoregressive support vector machine |
| url | https://www.frontiersin.org/articles/10.3389/fams.2025.1600278/full |
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