A Comparative Analysis of Statistical and Deep Learning Models for Global Temperature Anomalies Forecasting

Authors

  • Maryam Ibrahim Habadi Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
  • Shumukh AL-qahtani Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
  • Hadil Ibrahem Hariry Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
  • Mona Alshehri Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia

DOI:

https://doi.org/10.13052/jrss0974-8024.1825

Keywords:

SARIMA, machine learning, hybrid model, time series forecasting, temperature

Abstract

Global warming is among the most pressing environmental challenges, mainly driven by human-induced greenhouse gas emissions. Accurate forecasting of global temperature anomalies is essential for understanding climate trends and planning effective interventions. This study utilizes historical temperature anomaly data from 1940 to 2023. Aiming to compare the forecasting performance of several statistical and machine learning models: Seasonal Autoregressive Integrated Moving Average, Triple Exponential Smoothing, Temporal Convolutional Networks, Long Short-Term Memory, and two hybrid models, SARIMA-LSTM and SARIMA-TCN. Forecast accuracy was evaluated using Mean Squared Error, Mean Absolute Error, and Root Mean Squared Error. The TCN model demonstrated superior forecasting performance, achieving the lowest error across all metrics, followed by the SARIMA-LSTM hybrid model. The results support the combination of statistical and deep learning models for improved climate forecasting and offer valuable insights into future temperature trends amid global warming.

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Author Biographies

Maryam Ibrahim Habadi, Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Maryam Ibrahim Habadi is an Assistant Professor in the Department of Statistics at King Abdulaziz University, Jeddah, Saudi Arabia. She earned her Ph.D. in Statistics from the University of South Florida in 2019. Her research interests include statistical modeling, time series analysis, and applications of machine learning in environmental and health sciences. She has published several papers in international journals and conferences, focusing on climate change, Alzheimer’s disease, and predictive modeling.

Shumukh AL-qahtani, Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Shumukh AL-qahtani received her B.Sc. degree in Statistics from King Abdulaziz University, Jeddah, Saudi Arabia, in 2025. Her research interests include data analysis and time series forecasting, and she is currently working on publishing her first research paper in this field. She is also working at Baseera for Future Consultancy and Research.

Hadil Ibrahem Hariry, Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Hadil Ibrahem Hariry received her B.Sc. degree in Statistics from King Abdulaziz University in Jeddah, Saudi Arabia, in 2025. and is currently employed at Basiera Consulting & Research Co., Ltd., Subcontracted within the National Center for Meteorology. Her research interests include statistical modeling and deep learning techniques, with applications in time series and climate analysis. She has practical experience with analytical tools such as Python and R and aspires to advance research in climate change forecasting.

Mona Alshehri, Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Mona Alshehri received her M.Sc. degree in Statistics from King Abdulaziz University, Jeddah, Saudi Arabia, in 2025, with expertise in data analysis, statistical modeling, and machine learning. She has recently published two papers, and her research focuses on predictive analytics, time series forecasting, machine learning, and data-driven decision-making.

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Published

2025-10-15

How to Cite

Habadi, M. I. ., AL-qahtani, S. ., Hariry, H. I. ., & Alshehri, M. . (2025). A Comparative Analysis of Statistical and Deep Learning Models for Global Temperature Anomalies Forecasting. Journal of Reliability and Statistical Studies, 18(02), 371–398. https://doi.org/10.13052/jrss0974-8024.1825

Issue

2025: Vol 18 Iss 2

Section

Econometrics