  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.
