Forecasting demand of commodities after natural disasters

Authors:
Xiaoyan Xu;Yuqing Qi;Zhongsheng Hua
Affiliations:
School of Management, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China;School of Management, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China;School of Management, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Demand forecasting after natural disasters is especially important in emergency management. However, since the time series of commodities demand after natural disasters usually has a great deal of nonlinearity and irregularity, it has poor prediction performance of applying the traditional statistical and econometric models such as linear regression and autoregressive moving average (ARMA) to this kind of data. This paper tries to apply a hybrid forecasting method which is an integration of empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA). The EMD-ARIMA forecasting methodology is then applied to the prediction of agricultural products demand after the 2008 Chinese winter storms. Forecasting results indicate that EMD can improve the prediction accuracy of classical ARIMA forecasting method for demand of commodities after natural disasters.