A Hybrid Methodology for Short Term Temperature Forecasting

Wissam Abdallah , Nassib Abdallah (1) , Jean-Marie Marion (1, 2) , Mohamad Oueidat (3) , Pierre Chauvet (1, 2)

1 LARIS - Laboratoire Angevin de Recherche en Ingénierie des Systèmes
2 MAI - Mathématiques Appliquées et Informatique
3 LU - Lebanese University [Beirut]

Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.

Mots clés: ARIMA Auto Regressive Integrated Moving Average; Multiprocessing; Time Series Analysis; Weather Forecasting Model

Domaines: Informatique [cs]

Wissam Abdallah, Nassib Abdallah, Jean-Marie Marion, Mohamad Oueidat, Pierre Chauvet. A Hybrid Methodology for Short Term Temperature Forecasting. International Journal of Intelligence Science, 2020, 10 (3), ⟨10.4236/ijis.2020.103005⟩. ⟨hal-03525818⟩