Given the high inflation concerns in advanced economies, accurate forecasts of inflation rates are essential for well-informed fiscal and monetary policy decisions. This master's thesis analyses the use of different inflation forecasting models to better predict short, medium and long term changes. Traditional methods such as the SARIMAX model are identified, as well as modern models based on artificial intelligence and econometrics. A model based on neural networks with long short-term memory is proposed. AR(1) model is defined as a baseline. A reliable framework for the evaluation of defined models is established. It includes model testing using time series cross-validation on data from three different economies. The analysis shows deficiencies in all models. SARIMAX comes closest to the baseline model, while LSTM shows potential in forecasting with a larger amount of data. Universal inflation forecasting remains an open question until researchers have access to comprehensive macroeconomic and microeconomic data with models that work in this environment.
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