Forecasting Monthly Average Temperatures in Misurata City Using Seasonal Time Series Models
DOI:
https://doi.org/10.61952/jlabw.v1i4.307Keywords:
Time series, Forecasting, Multiplicative seasonal model, TemperatureAbstract
Global temperature variation is considered a central topic in climate change research, and forecasting future temperature anomalies is essential for developing strategies to mitigate climate-related risks. To better predict the long-term trend of global temperature change, In this study, the Box–Jenkins methodology was applied to analyze the seasonal time series of average temperatures for the city of Misurata during the period (2012–2024). This approach was utilized to develop a predictive model that accurately represents the study data, based on precise statistical criteria for selecting the most appropriate model. The selection was made according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The results of the analysis indicated that the most suitable and efficient model for representing the time series data is the multiplicative seasonal model of order SARIMA\left(1,1,0\right)\left(0,1,1\right)_{12}.
Based on this model, the monthly average temperatures for the years (2025–2026)were estimated. The predicted values demonstrated a noticeable consistency and alignment with the actual values of the time series, confirming the model’s efficiency in forecasting future trends. Furthermore, the study highlights the significance of applying the Box–Jenkins methodology in analyzing climatic phenomena characterized by cyclical and seasonal patterns.
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