  Increasingly high-stakes decisions are made using neural networks in order to
make predictions. Specifically, meteorologists and hedge funds apply these
techniques to time series data. When it comes to prediction, there are certain
limitations for machine learning models (such as lack of expressiveness,
vulnerability of domain shifts and overconfidence) which can be solved using
uncertainty estimation. There is a set of expectations regarding how
uncertainty should ``behave". For instance, a wider prediction horizon should
lead to more uncertainty or the model's confidence should be proportional to
its accuracy. In this paper, different uncertainty estimation methods are
compared to forecast meteorological time series data and evaluate these
expectations. The results show how each uncertainty estimation method performs
on the forecasting task, which partially evaluates the robustness of predicted
uncertainty.
