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Swarm Optimized Grey SVR and ARIMA for Modeling of Larceny-Theft Rate with Economic Indicators

Alwee, R. and Shamsuddin, S. M. and Sallehuddin, R. Swarm Optimized Grey SVR and ARIMA for Modeling of Larceny-Theft Rate with Economic Indicators. International Journal of Computational Intelligence and Applications, 16 (2). ISSN 1469-0268

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Official URL: http://dx.doi.org/10.1142/S1469026817500080

Abstract

As real world data, larceny-theft rates are most likely to have both linear and nonlinear components. A single model such as the linear or nonlinear model may not be sufficient to model the larceny-theft rate. Thus, a hybridization of the linear and nonlinear models is proposed for modeling the larceny-theft rate. The proposed model combines Support Vector Regression (SVR) and Autoregressive Integrated Moving Average (ARIMA) models. Particle swarm optimization is used to optimize the parameters of SVR and ARIMA models. The proposed model is equipped with features selection that combines grey relational analysis and SVR to choose the significant economic indicators for the larceny-theft rate. The experimental results show that the proposed model has better accuracy than the linear, nonlinear, and existing hybrid models in modeling the larceny-theft rate of United States.

Item Type:Article
Uncontrolled Keywords:economic indicators, grey relational analysis, larceny-theft rate
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:80897
Deposited By: Narimah Nawil
Deposited On:24 Jul 2019 08:10
Last Modified:24 Jul 2019 08:10

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