Abstract
One of the key issues in constructing monetary policy is accurate prediction of the inflation level. The complex behavior and non-linear nature of the financial markets makes it hard to forecast the inflation rate precisely. This paper introduces a hybrid model that attempts to forecast the inflation rate with a combination of a subtractive clustering technique and a fuzzy inference neural network to overcome the shortcomings of the individual methodologies. Selected macroeconomic factors were used to predict the historical CPI data from the US Markets. The results of the proposed hybrid model are measured in RMSE.
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