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

Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression