Demand Forecasting of Missile Spare Parts Based on Logistic Regression, Markov Process and Improved Grey Bootstrap Method
ZHAO Jianzhong1, XU Tingxue2, LI Haijun2, YIN Yantao3
1. Graduate Students' Brigade, Naval Aeronautical and Astronautical University, Yantai 264001, Shandong Province, China;2. Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical University, Yantai 264001, Shandong Province, China;3. Department of Scientific Research, Naval Aeronautical and Astronautical University, Yantai 264001, Shandong Province, China
Abstract：In order to enhance the forecasting accuracy of intermittent demands of missile spare parts, a combined forecasting model based on the logistic regression,Markov process and the improved improved grey Bootstrap method is proposed. This model splits the sample series into the explanatory series and the auto-correlated series. The probabilities of nonzero demands for the explanatory series in the lead time is estimated by a Logistic regression model, the probabilities of nonzero demands for the auto-correlated series in the lead time is estimated by the Markov process, and they are combined to obtain the probabilities of nonzero demands in the lead time. Finally the demand distribution is determinated by the improved grey Bootstrap method, where, the bootstrap sampling is made, and the data are matched by GM(1,1). Based on the principle of the grey bootstrap, the resample method is improved to avoid the bootstrap being repeatly resampled in a small sample case, and the GM(1,1) twice data fitting model is used to solve the problem of the credibility of the bootstrap's simulated result in a small sample case. Experimental results show that the combined forecasting model can significantly reduce the prediction errors and the method is effective, feasible and practical for forecasting the demands of missile spare parts.