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Inflation refers to an ongoing and overall comprehensive increase in the overall level
of goods and services price in the economy. Today; inflation, which is tried to be kept under control
by the central banks, is trying to ensure price stability, the continuous price changes that arise in
all the goods or services that consumers use includes. Undoubtedly in terms of economy, inflation
expectations are also ganing importance, except for rhe realized inflation. This situation makes it
necessary to predict the future vaules of inflation. In that case, a reliable estimate of the future
values of inflation in any country will create an entry in determining the policies that decisionmaker
units will implement on the economy.
The aim of this article is to predict inflation in the next period by using the Consumer Price Index
(CPI) data with two alternative techniques. It is also aimed to examine the prediction performances
of these two techniques in comparisons. Thus, the first of the two main objectives of the study is
to predict the future values of inflation with two alternative techniques. The second goal is to
determine which of these two techniques well compared to statistical and econometric criteria.
In this context, the estimated performance of both techniques was predicted by the 9-month
inflation, Box-Jenkins (ARIMA) and Artificial Neural Networks (ANN) in the April – December
2019 period, using CPI data consisting of 207 in the period of January 2002 – March 2019. In the
study, Eviews and Matlab programs were utilized.
International Data Science & Engineering Symposium
IDSES
Erkan IŞIĞIÇOK
Ramazan Öz
Savaş Tarkun