Open Access

ARTICLE

Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index

by Xiangyan Tang¹, Liang Wang³, Jieren Cheng¹, Jing Chen², Victor S. Sheng⁵

1 Key Laboratory of Internet Information Retrieval of Hainan Province, Hainan University, Haikou, 570228, China.
2 College of Information Science & Technology, Hainan University, Haikou, 570228, China.
3 School of Economic and Management, Hainan University, Haikou, 570228, China.
4 State key laboratory of Marine Resource Utilization in South China Sea, Haikou, 570228, China.
5 Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA .

* Corresponding Author: Jieren Cheng. Email: .

Computers, Materials & Continua 2019, 58(2), 463-491. https://doi.org/10.32604/cmc.2019.03816

Abstract

The accuracy of predicting the Producer Price Index (PPI) plays an indispensable role in government economic work. However, it is difficult to forecast the PPI. In our research, we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA (Autoregressive Integrated Moving Average Model) models. The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation. The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI, and produced three different sequences of fuzzy information granules, whose Support Vector Regression (SVR) machine forecast models were separately established for their Genetic Algorithm (GA) optimization parameters. Finally, the residual errors of the GA-SVR model were rectified through ARIMA modeling, and the PPI estimate was reached. Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models, including ARIMA, GRNN, and GA-SVR, following several comparative experiments. Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space.

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Citations