  Inflation is a major determinant for allocation decisions and its forecast is
a fundamental aim of governments and central banks. However, forecasting
inflation is not a trivial task, as its prediction relies on low frequency,
highly fluctuating data with unclear explanatory variables. While classical
models show some possibility of predicting inflation, reliably beating the
random walk benchmark remains difficult. Recently, (deep) neural networks have
shown impressive results in a multitude of applications, increasingly setting
the new state-of-the-art. This paper investigates the potential of the
transformer deep neural network architecture to forecast different inflation
rates. The results are compared to a study on classical time series and machine
learning models. We show that our adapted transformer, on average, outperforms
the baseline in 6 out of 16 experiments, showing best scores in two out of four
investigated inflation rates. Our results demonstrate that a transformer based
neural network can outperform classical regression and machine learning models
in certain inflation rates and forecasting horizons.
