Joint Handling of Data and Model Uncertainty for Interpretable Interval Prediction

The presence of uncertainty, such as data uncertainty (e.g., noise) and model uncertainty (e.g., parameters), directly impacts the efficacy of prediction systems. Recent research is increasingly focusing on the explicit modelling of uncertainty within the prediction by deriving an interval-valued, rather than a point-valued prediction. The aim here is to explicitly capture uncertainty within data and model and map it systematically to the prediction outputs, in turn providing usable bounds on the expected prediction. Such bounds can convey crucial insight, such as the expected amount of renewable energy generation expected-enabling the minimal-but sufficient use of fossil fuels and other energy sources. This paper explores the viability of recent advances within non-singleton fuzzy systems to support the efficient and effective prediction of such interval-bounds, while providing an interpretable prediction model. The latter is desirable, as it affords experts not only the ability to validate the model, but allows them to interact with and change the model to adapt to unforeseen circumstances. For example, in a renewal energy context, priming a prediction system for uncommon weather patters can support its effectiveness. The proposed approach integrates both data and model uncertainty into the system recursively, generating systematic prediction intervals. To demonstrate the potential of the approach in integrating these-while supporting interpretability/interactivity, we show a series of synthetic experiments using the Mackey glass time series. The results highlight the capacity of the approach to preserve and recursively model the output uncertainty for multi-step ahead predictions. Further, the experiments demonstrate how the proposed system can provide explanations for given predictions, providing the basis for supporting expert interaction.