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FORECASTING CPI USING A NEURAL NETWORK WITH FUZZY INFERENCE SYSTEM

Consumer price index (CPI) is an index of the cost of all goods and services of a typical consumer. CPI is a key measure of inflation that relates the rise in prices over a period of time. It is a fixed quantity price index and is used as an economic indicator, more specific as an inflationary indicator. As the most widely used measure of inflation, CPI is an indicator of the effectiveness of government policies. The business executives, labour leaders and private citizens use the index as a guide in making economic decisions. It is also called cost-of-living index. This work presents an attempt to forecast CPI using an adaptive model, a neural network with fuzzy inference system. The adaptive models transform the internal behavior structure to include changes in the outside world. The data used for the model is related to Greece and is from OECD Factbook 2008. The value of the year 2000 is consider for comparison and is equaled with 100. The results are compared with traditional forecasting models.