A Hybrid Forecasting Model Based on Chaotic Mapping and Improved v-Support Vector Machine

Authors:
Qi Wu;Hongsen Yan;Hongbing Yang
Affiliations:
-;-;-
Venue:
ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
Year:
2008

Citing 0
Cited 4

Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM

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A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization

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Regression application based on fuzzy ν-support vector machine in symmetric triangular fuzzy space

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Hybrid model based on SVM with Gaussian loss function and adaptive Gaussian PSO

Engineering Applications of Artificial Intelligence

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Abstract

Aiming at the product demand series with multi-dimension, small samples, nonlinearity and multi-apex in manufacturing enterprise, chaos theory is combined with support vector machine, and a kind of chaotic support vector machine named Cv-SVM is proposed. And then, a product demand forecasting method and its relevant parameter-choosing algorithm are put forward. The results of application in car demand forecasting show that the forecasting method based on Cv-SVM is effective and feasible.