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Comparative evaluation of point process forecasts

Author

Listed:
  • Jonas R. Brehmer

    (Heidelberg Institute for Theoretical Studies)

  • Tilmann Gneiting

    (Heidelberg Institute for Theoretical Studies
    Karlsruhe Institute of Technology (KIT))

  • Marcus Herrmann

    (University of Naples Federico II, Complesso di Monte Sant’Angelo)

  • Warner Marzocchi

    (University of Naples Federico II, Complesso di Monte Sant’Angelo)

  • Martin Schlather

    (University of Mannheim)

  • Kirstin Strokorb

    (Cardiff University)

Abstract

Stochastic models of point patterns in space and time are widely used to issue forecasts or assess risk, and often they affect societally relevant decisions. We adapt the concept of consistent scoring functions and proper scoring rules, which are statistically principled tools for the comparative evaluation of predictive performance, to the point process setting, and place both new and existing methodology in this framework. With reference to earthquake likelihood model testing, we demonstrate that extant techniques apply in much broader contexts than previously thought. In particular, the Poisson log-likelihood can be used for theoretically principled comparative forecast evaluation in terms of cell expectations. We illustrate the approach in a simulation study and in a comparative evaluation of operational earthquake forecasts for Italy.

Suggested Citation

  • Jonas R. Brehmer & Tilmann Gneiting & Marcus Herrmann & Warner Marzocchi & Martin Schlather & Kirstin Strokorb, 2024. "Comparative evaluation of point process forecasts," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(1), pages 47-71, February.
    • Handle: RePEc:spr:aistmt:v:76:y:2024:i:1:d:10.1007_s10463-023-00875-5
    DOI: 10.1007/s10463-023-00875-5
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    References listed on IDEAS

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    1. Werner Ehm & Tilmann Gneiting & Alexander Jordan & Fabian Krüger, 2016. "Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 505-562, June.
    2. Yosihiko Ogata, 1998. "Space-Time Point-Process Models for Earthquake Occurrences," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(2), pages 379-402, June.
    3. A. Baddeley & R. Turner & J. Møller & M. Hazelton, 2005. "Residual analysis for spatial point processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 617-666, November.
    4. Rafael M Frongillo & Ian A Kash, 2021. "Elicitation complexity of statistical properties [A characterization of scoring rules for linear properties]," Biometrika, Biometrika Trust, vol. 108(4), pages 857-879.
    5. Tilmann Gneiting & Roopesh Ranjan, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 411-422, July.
    6. J. Chen & A. G. Hawkes & E. Scalas & M. Trinh, 2018. "Performance of information criteria for selection of Hawkes process models of financial data," Quantitative Finance, Taylor & Francis Journals, vol. 18(2), pages 225-235, February.
    7. Frederic Paik Schoenberg & Marc Hoffmann & Ryan J. Harrigan, 2019. "A recursive point process model for infectious diseases," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1271-1287, October.
    8. Matthew C. Gerstenberger & Stefan Wiemer & Lucile M. Jones & Paul A. Reasenberg, 2005. "Real-time forecasts of tomorrow's earthquakes in California," Nature, Nature, vol. 435(7040), pages 328-331, May.
    9. Natalia Nolde & Johanna F. Ziegel, 2016. "Elicitability and backtesting: Perspectives for banking regulation," Papers 1608.05498, arXiv.org, revised Feb 2017.
    10. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    11. Alexander Dawid & Monica Musio, 2014. "Theory and applications of proper scoring rules," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 169-183, August.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Mohler, G. O. & Short, M. B. & Brantingham, P. J. & Schoenberg, F. P. & Tita, G. E., 2011. "Self-Exciting Point Process Modeling of Crime," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 100-108.
    14. Jiancang Zhuang & Jorge Mateu, 2019. "A semiparametric spatiotemporal Hawkes‐type point process model with periodic background for crime data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(3), pages 919-942, June.
    15. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    16. Frédéric Lavancier & Jesper Møller & Ege Rubak, 2015. "Determinantal point process models and statistical inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(4), pages 853-877, September.
    17. Gneiting, Tilmann & Ranjan, Roopesh, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 411-422.
    18. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
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