Computational Results and Evaluation

  • Steffen Christ


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.


Forecast Error Forecast Accuracy Forecast Performance Informed Learning Forecast Horizon 
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Copyright information

© Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

Authors and Affiliations

  • Steffen Christ

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