Using machine learning methods to examine factors affecting inflation: The example of Türkiye

Inflation is caused by the growing gap between the amount of money actively involved and the sum of products and services available for purchase. It is an economic and monetary process that manifests itself as a constant rise in prices, a fall in the current value of money. Inflation is a subject that keeps itself constantly updated in our country and around the world. The main purpose of the central banks, which are dependent on countries in the world and continue their activities, on the economy is to ensure price stability permanently. In recent years, artificial intelligence techniques have been used more and more in order to consistently predict the value of inflation in the future and to make future studies with the forecasts obtained. When the predictions made for the future based on the data obtained in the past are combined with artificial intelligence techniques, they reveal results closer to reality. Despite the increasing application areas with artificial intelligence techniques, it is very difficult to make predictions and carry out future studies in the field of finance and economy with the estimation method. The high uncertainty and instability in the financial sector and the volatility on the data obtained reveal the most important reason for this situation. Despite this uncertainty in the financial sector, it is very important for the country's economy, companies and households to make an inflation forecast with high accuracy. The aim of this study is to estimate inflation in the Turkish economy with time series analysis by using Vector Autoregression (VAR) model among artificial intelligence and machine learning techniques and LSTM (Long Short Term Memory) model, which is one of the artificial neural networks types, on a python computer program. The aim of this study is to use the Vector Autoregression (VAR) model, gradient boost regression (GBR) and LSTM (Long Short Term Memory) model, which is one of the classical deep learning types, on the python computer program within the scope of artificial intelligence and machine learning techniques. To make comparisons between models by making inflation forecasts in the Turkish economy in 4 different time intervals with time series analysis. With this study, the estimation made by the LSTM model showed the most successful result among the applied models when compared in terms of MAPE, MSE and RMSE statistical analyzes. It has been observed that the irregular increase in the inflation value within the country in the recent periods directly affects the success level of the models.