Computational Results and Evaluation
Following the introduction of the Bayesian self-learning forecasting scheme underlying this work (see previous Chapters 4 – 6), this chapter now provides the computational results and takes a look at the overall predictive performance of the model in Section 7.1 as well as its sensitivity to using informative priors, changing learning window sizes and different forecast granularities in Section 7.2.
KeywordsForecast Error Forecast Accuracy Forecast Performance Informed Learning Forecast Horizon
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