Prediction of Multidimensional Time Series Based on GS-RSR -SVR and Its Application in Agricultural Economy

Y. G. XIE1,2, H. Y. ZHANG1,2,3, H. Y. WANG4, L. F. Wang1,3 and Zh. M. YUAN1,3
1 Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Changsha 410128, China
2 Hunan Agricultural University, College of Information Science and Technology, Changsha 410128, China
3 Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Changsha 410128, China
4 Kansas State University, Department of Statistics, Manhattan, Kansas 66506, USA


XIE, Y. G., H. Y. ZHANG, H. Y. WANG, L. F. Wang and ZH. M. YUAN, 2013. Prediction of multidimensional time series based on GS-RSR-SVR and its application in agricultural economy. Bulg. J. Agric. Sci.,19: 1327-1336


This paper proposes a method that creatively applies a Geo-statistics tool (GS) to complete fast and adequate order determination and introduces a novel algorithm, named Reasonable Sample Rejection (RSR) to realize rational sample selection. Then, combined with Support Vector Machine Regression (SVR), a high precision non-linear prediction method named GSRSR-SVR is proposed for multidimensional time series. The main steps of the novel method includes: 1) determine the order for the dependent variable of the training samples based on one-dimensional GS aftereffect duration (range), 2) screen the independent variables according to Leave-One-Out Cross Validation (LOOCV) based on the minimum Mean Squared Error (MSE), 3) reject some oldest training samples based on the minimum correlation coefficient of fitting absolute relative error of training sets of different rejected sizes and sample number. Three real-world datasets was used to test the effectiveness of GSRSR-SVR. The results show that GS-RSR-SVR has higher prediction precision and more stable prediction ability than MLR, ARIMA, CAR, BPNN, SVR and SVR-CAR.

Key words: multidimensional time series; geo-statistics tool; reasonable sample rejection; support vector machine regression; prediction
Abbreviations: GS: geo-statistics; RSR: reasonable sample rejection; SVR: support vector machine regression; LOOCV: leave-one-out cross validation; MSE: mean squared error; MLR: multi-level recursive; ARIMA: autoregressive integrated moving average; CARMA: controlled autoregressive integrating moving average; CAR: controlled autoregressive; ANN: artificial neural network; IGAOV: index of gross agricultural output value; MAPE: mean absolute percentage error; APE: absolute percentage error; LLD: log-linear de-trending

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