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Time series forecasting has a fundamental importance in various practical domains. Many models have been proposed in literature to model and predict the Time Series Data (TSD) efficiently. As the modeling and prediction depends on the nature of TSD, one model may not be opt for all applications. This paper presents a hybrid model based on Particle Swarm Optimization (PSO) with Least Square Support...
Crustal deformation time series is a significant information source during the researches on continental deformation. In order to simulate the low frequency linear components which reflect dynamic trend as well as the high-frequency non-linear components which reflect disturbance, a prediction model based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented. With optimization...
The prediction of chaotic time series is performed by least square support vector machine (LS-SVM) based on particle swarm optimization (PSO). The main objective of this approach is to increase the accuracy of the chaotic time series prediction. For the generation performance of LS-SVM depending on a good setting of its parameters, PSO is adopted to choose the global optimum parameters of LS-SVM automatically...
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