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Decision Analytics with Heatmap Visualization for Multi-step Ensemble Data

Author

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  • Cornelius Köpp
  • Hans-Jörg Mettenheim
  • Michael Breitner

Abstract

Today’s forecasting techniques, which are integrated into several information systems, often use ensembles that represent different scenarios. Aggregating these forecasts is a challenging task: when using the mean or median (common practice), important information is lost, especially if the underlying distribution at every step is multimodal. To avoid this, the authors present a heatmap visualization approach. It is easy to visually distinguish regions of high activity (high probability of realization) from regions of low activity. This form of visualization allows to identify splitting paths in the forecast ensemble and adds a “third alternative” to the decision space. Most forecast systems only offer “up” or “down”: the presented heatmap visualization additionally introduces “don’t know”. Looking at the heatmap, regions can be identified in which the underlying forecast model cannot predict the outcome. The authors present a software prototype with interactive visualization to support decision makers and discuss the information gained by its use. The prototype has already been presented to and discussed with researchers and practitioners. Copyright Springer Fachmedien Wiesbaden 2014

Suggested Citation

  • Cornelius Köpp & Hans-Jörg Mettenheim & Michael Breitner, 2014. "Decision Analytics with Heatmap Visualization for Multi-step Ensemble Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(3), pages 131-140, June.
    • Handle: RePEc:spr:binfse:v:6:y:2014:i:3:p:131-140
    DOI: 10.1007/s12599-014-0326-4
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    1. Diethard Klatte & Hans-Jakob Lüthi & Karl Schmedders (ed.), 2012. "Operations Research Proceedings 2011," Operations Research Proceedings, Springer, edition 127, number 978-3-642-29210-1, March.
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    3. G P Zhang & V L Berardi, 2001. "Time series forecasting with neural network ensembles: an application for exchange rate prediction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(6), pages 652-664, June.
    4. Hansen, Bruce E., 2008. "Least-squares forecast averaging," Journal of Econometrics, Elsevier, vol. 146(2), pages 342-350, October.
    5. Ivo Welch, 2001. "The Equity Premium Consensus Forecast Revisited," Cowles Foundation Discussion Papers 1325, Cowles Foundation for Research in Economics, Yale University.
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    Cited by:

    1. Christoph Gleue & Dennis Eilers & Hans-Jörg Mettenheim & Michael H. Breitner, 2019. "Decision Support for the Automotive Industry," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 385-397, August.

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