  Reliable gas price forecasts are an essential information for gas and energy
traders, for risk managers and also economists. However, ahead of the war in
Ukraine Europe began to suffer from substantially increased and volatile gas
prices which culminated in the aftermath of the North Stream 1 explosion. This
shock changed both trend and volatility structure of the prices and has
considerable effects on forecasting models. In this study we investigate
whether modern machine learning methods such as neural networks are more
resilient against such changes than statistical models such as autoregressive
moving average (ARMA) models with conditional heteroskedasticity, or
copula-based time series models. Thereby the focus lies on interval forecasting
and applying respective evaluation measures. As data, the Front Month prices
from the Dutch Title Transfer Facility, currently the predominant European
exchange, are used. We see that, during the shock period, most models
underestimate the variance while overestimating the variance in the after-shock
period. Furthermore, we recognize that, during the shock, the simpler models,
i.e. an ARMA model with conditional heteroskedasticity and the multilayer
perceptron (a neural network), perform best with regards to prediction interval
coverage. Interestingly, the widely-used long-short term neural network is
outperformed by its competitors.
