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Nowadays, owing to the accelerated growth in the development of information technology, the aviation industry plays a prominent role in Chinese economy. Therefore, the accurate prediction for the number of international airlines helps in making major decisions in technological reform. In order to analyze the number of international airlines in China efficiently, in this paper, fuzzy adaptive PSO algorithm...
The aim of this paper is to predict the inventory of the relevant upstream enterprises in supply chain. The support vector machine, a novel artificial intelligence-based method developed from statistical learning theory, is adopted herein to establish a short-term stage forecasting model. However, take the fact into account that demand signal is affected by variant random factors and behaves big uncertainty,...
Time series analysis is an important and complex problem in machine learning. Support vector machine (SVM) has recently emerged as a powerful technique for solving problems in regression, but its performance mainly depends on the parameters selection of it. Parameters selection for SVM is very complex in nature and quite hard to solve by conventional optimization techniques, which constrains its application...
In this paper, a new approach to ARMA model identification using evolutionary particle swarm optimization (PSO) algorithm has been proposed. ARMA is a popular method to analyze stationary univariate time series data. Stationarity checking, model identification, model estimation and model checking are usually four main stages to build an ARMA model and model identification is the most important stage...
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