Inflation estimation with artificial neural networks

Nowadays, artificial intelligence techniques have been used in comparison to the statistical methods which are traditional in economics and finance. Based on the data of the previous period, the predictions for the future showed the results closer to reality with artificial intelligence techniques and found its application area in every field. Forecasting in the field of economics and finance is difficult to predict. High uncertainty and volatility are the reasons for the estimation of the area. The fact that the estimated values are found to be the closest to the reality is very important for the high-risk economy and finance area. As a result of the studies conducted in the literature, it has been observed that the artificial neural network method is successful compared to many other applications. In this study, Anfis model and K-means algorithm were used on Matlab. The data set was selected from the factors affecting inflation. Anfis and K-means selected the most effective inputs from the available data set and applied the model to predict the future.