Integrating Machine Learning and Time Series Models for Spatiotemporal Temperature Prediction: A Case Study from Apulia, Italy

Temperature plays a critical role in climate systems and resource management. Understanding spatiotemporal evolution of the temperature is vital for effective climate adaptation and resource management. Traditional models often treat spatial and temporal aspects separately, limiting their ability to capture the full correlation between these dimensions. This study evaluates various time series and machine learning models, including Holt-Winters, SARIMA, TSLM, NNAR, and ANN, using a daily dataset from 30 meteorological stations in Apulia region (Italy) from 1982 to 2023. These models are assessed based on RMSE and MAE metrics. The best models are then integrated with spatiotemporal kriging of the residual data, with results showing that the hybrid approach outperforms traditional methods. This generated high-resolution predictive maps provide valuable insights into temperature trends, supporting better decision-making in agriculture, water management, and climate resilience.