A Grey SVM based model for Patent Application Filings Forecasting

Abstract

Tracking historical levels as well as estimating future levels of patent applications is an ongoing activity of considerable significance. The patent applications filings (PAF) are complex to conduct due to its nonlinearity of influenced factors. Support vector machines (SVM) have been successfully employed to solve nonlinear regression and time series problems. Grey theory is a truly multidisciplinary and generic theory that deals with systems that are characterized by poor information and/or for which information is lacking. Grey systemtheory successfully utilizes accumulated generating data instead of original data to build forecasting model, which makes raw data stochastic weak, or reduces noise influence in a certain extent. However, the application combining grey system theory and SVM for PAF forecasting is rare. In this study, a grey support vector machines with genetic algorithms (GSVMG) is proposed to forecast PAF. In addition, Grey system is used to add a grey layer before neural input layer and white layer after SVM layer. Genetic algorithms (GAs) are used to determine free parameters of support vector machines. Evaluation method has been used for comparing the performance of forecasting techniques. The experiments show that the GSVMG model is outperformed grey model and SVM with genetic algorithms (SVMG) model and PAF forecasting based on GSVMG is of validity and feasibility.