ORIGINAL RESEARCH article
Front. Appl. Math. Stat.
Sec. Numerical Analysis and Scientific Computation
Volume 11 - 2025 | doi: 10.3389/fams.2025.1600278
Modeling and Forecasting of the High-Dimensional Time Series Data with Functional Data Analysis and Machine Learning Approaches
Provisionally accepted
- 1Public Authority for Applied Education and Training, Kuwait City, Kuwait
- 2Department of Statistics and Operations Research, College of Science, Kuwait University, Safat, Kuwait
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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.
Keywords: Functional autoregressive, Functional time series, artificial neural network, neural network autoregressive, Support vector machine
Received: 26 Mar 2025; Accepted: 21 Jul 2025.
Copyright: © 2025 Alkhezi, Alkhezi and Shafee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: AHMAD Shafee, Public Authority for Applied Education and Training, Kuwait City, Kuwait
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.