Forecasting model based on information-granulated GA-SVR and ARIMA for producer price index

Xiangyan Tang, Liang Wang, Jieren Cheng, Jing Chen, Victor S. Sheng

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish
Pages (from-to)463-491
Number of pages29
JournalComputers, Materials and Continua
Volume58
Issue number2
DOIs
StatePublished - 2019

Keywords

  • ARIMA model
  • Data analysis
  • Fuzzy information granulation
  • Producer price index
  • Support vector model

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