IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v25y2009i1p48-61.html
   My bibliography  Save this article

Forecast accuracy measures for exception reporting using receiver operating characteristic curves

Author

Listed:
  • Gorr, Wilpen L.

Abstract

The exception principle of management reporting suggests that, under ordinary conditions, operational staff persons make decisions, but that the same staff refer decisions to upper-level managers under exceptional conditions. Forecasts of large changes or extreme values in product or service demand are potential triggers for such reporting. Seasonality estimates in univariate forecast models and leading independent variables in multivariate forecast models are among the approaches to forecasting exceptional demand, a forecast activity that this paper identifies as requiring new accuracy measures based on the tails of sampled forecast error distributions, rather than conventional measures which use the central tendency. For this purpose, the paper introduces the application of the receiver operating characteristic (ROC) framework, which has been used for the assessment of exceptional behavior in many fields. In a case study on serious violent crime in Pittsburgh, Pennsylvania, the simplest, non-naïve univariate forecast method is best for forecasting ordinary conditions using conventional forecast accuracy measures, but the most complex multivariate model is best for forecasting exceptional conditions using ROC forecast accuracy measures.

Suggested Citation

  • Gorr, Wilpen L., 2009. "Forecast accuracy measures for exception reporting using receiver operating characteristic curves," International Journal of Forecasting, Elsevier, vol. 25(1), pages 48-61.
    • Handle: RePEc:eee:intfor:v:25:y:2009:i:1:p:48-61
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169-2070(08)00144-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Withycombe, Richard, 1989. "Forecasting with combined seasonal indices," International Journal of Forecasting, Elsevier, vol. 5(4), pages 547-552.
    2. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    3. Lauge Valentin, 2007. "Use Scaled Errors Instead of Percentage Errors in Forecast Evaluations," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 7, pages 17-22, Summer.
    4. Bunn, Derek W. & Vassilopoulos, A. I., 1993. "Using group seasonal indices in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 9(4), pages 517-526, December.
    5. Russell L. Ackoff, 1967. "Management Misinformation Systems," Management Science, INFORMS, vol. 14(4), pages 147-156, December.
    6. Robert Simons, 1991. "Strategic orientation and top management attention to control systems," Strategic Management Journal, Wiley Blackwell, vol. 12(1), pages 49-62, January.
    7. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    8. Gorr, Wilpen & Olligschlaeger, Andreas & Thompson, Yvonne, 2003. "Short-term forecasting of crime," International Journal of Forecasting, Elsevier, vol. 19(4), pages 579-594.
    9. Peter Deneef & Daniel L. Kent, 1993. "Using Treatment-tradeoff Preferences to Select Diagnostic Strategies," Medical Decision Making, , vol. 13(2), pages 126-132, June.
    10. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
    11. Stephan Kolassa & Wolfgang Schütz, 2007. "Advantages of the MAD/Mean Ratio over the MAPE," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 40-43, Spring.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mihaela Bratu, 2012. "A Strategy to Improve the Survey of Professional Forecasters (SPF) Predictions Using Bias-Corrected-Accelerated (BCA) Bootstrap Forecast Intervals," International Journal of Synergy and Research, ToKnowPress, vol. 1(2), pages 45-59.
    2. Mihaela Bratu (Simionescu), 2013. "How to Improve the SPF Forecasts?," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 9(2), pages 153-165, April.
    3. Emilian Dobrescu, 2014. "Attempting to Quantify the Accuracy of Complex Macroeconomic Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-21, December.
    4. BRATU SIMIONESCU, Mihaela, 2012. "Two Quantitative Forecasting Methods For Macroeconomic Indicators In Czech Republic," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 3(1), pages 71-87.
    5. Mihaela BRATU (SIMIONESCU), 2012. "A Strategy To Improve The Gdp Index Forcasts In Romania Using Moving Average Models Of Historical Errors Of The Dobrescu Macromodel," Romanian Journal of Economics, Institute of National Economy, vol. 35(2(44)), pages 128-138, December.
    6. Bratu Mihaela, 2013. "An Evaluation Of Usa Unemployment Rate Forecasts In Terms Of Accuracy And Bias. Empirical Methods To Improve The Forecasts Accuracy," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 170-180, February.
    7. Alex Reinhart & Joel Greenhouse, 2018. "Self‐exciting point processes with spatial covariates: modelling the dynamics of crime," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1305-1329, November.
    8. Mohler, George, 2014. "Marked point process hotspot maps for homicide and gun crime prediction in Chicago," International Journal of Forecasting, Elsevier, vol. 30(3), pages 491-497.
    9. Gorr, Wilpen L. & Schneider, Matthew J., 2013. "Large-change forecast accuracy: Reanalysis of M3-Competition data using receiver operating characteristic analysis," International Journal of Forecasting, Elsevier, vol. 29(2), pages 274-281.
    10. Huddleston, Samuel H. & Porter, John H. & Brown, Donald E., 2015. "Improving forecasts for noisy geographic time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1810-1818.
    11. Schneider, Matthew J. & Gorr, Wilpen L., 2015. "ROC-based model estimation for forecasting large changes in demand," International Journal of Forecasting, Elsevier, vol. 31(2), pages 253-262.
    12. Constantin Mitru? & Mihaela Bratu (Simionescu), 2013. "The Indicators’ Inadequacy and the Predictions’ Accuracy," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 9(4), pages 430-442, August.
    13. Mihaela Simionescu, 2015. "The Improvement of Unemployment Rate Predictions Accuracy," Prague Economic Papers, Prague University of Economics and Business, vol. 2015(3), pages 274-286.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.
    2. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
    3. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.
    4. Emilian Dobrescu, 2014. "Attempting to Quantify the Accuracy of Complex Macroeconomic Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-21, December.
    5. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    6. Athanasopoulos, George & Kourentzes, Nikolaos, 2023. "On the evaluation of hierarchical forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1502-1511.
    7. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    8. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    9. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    10. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    11. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    12. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.
    13. George Athanasopoulos & Nikolaos Kourentzes, 2021. "On the Evaluation of Hierarchical Forecasts," Monash Econometrics and Business Statistics Working Papers 10/21, Monash University, Department of Econometrics and Business Statistics.
    14. George Athanasopoulos & Nikolaos Kourentzes, 2020. "On the Evaluation of Hierarchical Forecasts," Monash Econometrics and Business Statistics Working Papers 2/20, Monash University, Department of Econometrics and Business Statistics.
    15. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    16. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    17. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
    18. Bloom, David E. & Canning, David & Fink, Gunther & Finlay, Jocelyn E., 2007. "Does age structure forecast economic growth?," International Journal of Forecasting, Elsevier, vol. 23(4), pages 569-585.
    19. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    20. Stefanescu, Răzvan & Dumitriu, Ramona, 2017. "Ajustarea seriilor de timp financiare,Partea întâi [Smoothing of financial time series, Part 1]," MPRA Paper 78329, University Library of Munich, Germany, revised 15 Apr 2017.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:25:y:2009:i:1:p:48-61. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.