A Combined DE Algorithm with SARIMA for Modeling and Predicting the Incidence of Zoonotic Cutaneous Leishmaniasis in Msila Province, Algeria

N. Frissou, M.T. Kimour, S. Selmane

Abstract


Time series forecasting is a valuable tool to recognize and control the behavior of various practical systems, based on the data in a certain period of time. One of the most widely used method in time series forecasting is ARIMA (AutoRegressive Integrated Moving Average), and SARIMA, which extends ARIMA to handle the seasonal data.  However, ARIMA-SARIMA has a weakness in determining the optimal model. In this research, we present a combined approach of the differential evolution (DE) algorithm and SARIMA model (p, d, q)´(P, D, Q), allowing to  calculate the smallest Akaike Information Criterion (AIC) value, which represents a quality metrics of the statistical model. In doing such combination, we show that better accuracy and more convergence speed up of the calculated ARIMA-SARIMA model can be obtained. The data used in the study are monthly data of the Zoonotic Cutaneous Leishmaniasis, from January 2013 to December 2020 in Msila Province, Algeria.  


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