Using Seasonal Autoregressive Integrated Moving Average Model to Forecasting the Monthly Temperatures: A Case Study Tripoli Area at the North West of Libya
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Time Series, Box-Jenkins Models, Forecasting, Average, Temperature, SARIMA Model
Abstract
The Box-Jenkins methodology of time series analysis is considered one of the most important predictive models. These models are characterized by high accuracy and flexibility in their diagnosis and description of the future climate phenomena and variables. In this paper the Box-Jenkins methodology has been applied to analyze the average monthly climate data for the Tripoli area, which is located in northwestern Libya. The considered data represent monthly data observed over the period (1961-2003) inclusive. Specialized software has been used for data analysis. After comparison processes among several different standard models that belong to ARIMA models, it has been observed that the SARIMA(1,0,5)(0,0,2)12 model is most appropriate and efficient to predict the monthly temperatures. According to the estimation results using this model, the monthly temperature of the Tripoli area was predicted for the period from January 2004 to December 2005. The predicted values of the monthly temperature have shown to be consistent with those of the original series.
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