A Short-Term Forecasting Model With Inhibiting Normal Distribution Noise of Sale Series

In view of the dissatisfactory forecasting capability of standard support vector machine (SVM) for product sale series with normal distribution noises, a new SVM, called g-SVM, with Gaussian function used as its loss function, is proposed. It is theoretically proved that an adjustable parameter of g-SVM is equal to not only the upper bound of the proportion of erroneous samples to total samples but also the lower bound of the proportion of support vectors to total samples; in other words, the number of erroneous samples is fewer than or equal to that of support vectors. A new version of particle swarm optimization (PSO) with the integration of Logistic mapping and standard PSO is proposed for an optimal parameter combination of g-SVM. With the above, a short-term intelligent forecasting method based on g-SVM and the proposed PSO is then put forth. The results of its application to car-sales forecasting indicate that the forecasting method is feasible and effective.