Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Emrouznejad, Ali; Rostami-Tabar, Bahman; Petridis, Konstantinos (2016)
Publisher: Elsevier BV
Journal: Technological Forecasting and Social Change
Languages: English
Types: Article
Subjects: Business and International Management, Applied Psychology, Management of Technology and Innovation
To compare the accuracy of different forecasting approaches an error measure is required. Many error measures have been proposed in the literature, however in practice there are some situations where different measures yield different decisions on forecasting approach selection and there is no agreement on which approach should be used. Generally forecasting measures represent ratios or percentages providing an overall image of how well fitted the forecasting technique is to the observations. This paper proposes a multiplicative Data Envelopment Analysis (DEA) model in order to rank several forecasting techniques. We demonstrate the proposed model by applying it to the set of yearly time series of the M3 competition. The usefulness of the proposed approach has been tested using the M3-competition where five error measures have been applied in and aggregated to a single DEA score.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Abdel-Khalik, A.R., El-Sheshai, K.M., 1983. Sales revenues: time-series properties and predictions. J. Forecast. 2, 351-362.
    • Athanasopoulos, G., Hyndman, R.J., 2011. The value of feedback in forecasting competitions. Int. J. Forecast. 27, 845-849.
    • Banker, R.D., Maindiratta, A., 1986. Piecewise loglinear estimation of efficient production surfaces. Manag. Sci. 32, 126-135.
    • Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 30, 1078-1092.
    • Brazdil, P.B., Soares, C., Da Costa, J.P., 2003. Ranking learning algorithms: using IBL and meta-learning on accuracy and time results. Mach. Learn. 50, 251-277.
    • Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429-444.
    • Charnes, A., Cooper, W.W., Seiford, L.M., Stutz, J., 1982. A multiplicative model for efficiency analysis. Socio Econ. Plan. Sci. 16, 24.
    • Chatfield, C., 2013. The Analysis of Time Series: An Introduction. CRC Press.
    • Chen, Y., 2005. Measuring super-efficiency in DEA in the presence of infeasibility. Eur. J. Oper. Res. 161, 545-551.
    • Collopy, F., Armstrong, J.S., 1992. Expert opinions about extrapolation and the mystery of the overlooked discontinuities. Int. J. Forecast. 8, 575-582.
    • Cook, W.D., Seiford, L.M., 2009. Data envelopment analysis (DEA)-thirty years on. Eur. J. Oper. Res. 192, 1-17.
    • Cook, W.D., Zhu, J., 2013. DEA Cobb-Douglas frontier and cross-efficiency. J. Oper. Res. Soc. 65, 265-268.
    • Cook, W.D., Liang, L., Zha, Y., Zhu, J., 2009. A modified super-efficiency DEA model for infeasibility. J. Oper. Res. Soc. bf 60, 276-281.
    • Davydenko, A., Fildes, R., 2013. Measuring forecasting accuracy: the case of judgmental adjustments to SKU-level demand forecasts. Int. J. Forecast. 29, 510-522.
    • De Gooijer, J.G., Hyndman, R.J., 2006. 25 years of time series forecasting. Int. J. Forecast. 22, 443-473.
    • Duong, Q.P., 1988. Model selection and ranking: an AHP approach to forecasts combination. Math. Comput. Model. 11, 282-285.
    • Emrouznejad, A., Amin, G.R., 2009. DEA models for ratio data: convexity consideration. Appl. Math. Model. 33, 486-498.
    • Emrouznejad, A., De Witte, K., 2010. COOPER-framework: a unified process for nonparametric projects. Eur. J. Oper. Res. 207, 1573-1586.
    • Emrouznejad, A., Cabanda, E., Gholami, R., 2010. An alternative measure of the ICT-opportunity index. Inf. Manag. 47, 246-254.
    • Fildes, R., Wei, Y., Ismail, S., 2011. Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures. Int. J. Forecast. 27, 902-922.
    • Geurts, M.D., Patrick Kelly, J., 1986. Forecasting retail sales using alternative models. Int. J. Forecast. 2, 261-272.
    • Hollingsworth, B., Smith, P., 2003. Use of ratios in data envelopment analysis. Appl. Econ. Lett. 10, 733-735.
    • Hyndman, R.J., Athanasopoulos, G., 2014. Forecasting: Principles and Practice. OTexts.
    • Hyndman, R.J., Koehler, A.B., 2006. Another look at measures of forecast accuracy. Int. J. Forecast. 22, 679-688.
    • Inman, O.L., Anderson, T.R., Harmon, R.R., 2006. Predicting US jet fighter aircraft introductions from 1944 to 1982: a dogfight between regression and TFDEA. Technol. Forecast. Soc. Chang. 73, 1178-1187.
    • Kitchenham, B.A., Pickard, L.M., MacDonell, S.G., Shepperd, M.J., 2001. What Accuracy Statistics really Measure [Software Estimation]. SoftwareIEE Proceedings vol. 148, No. 3. IET, pp. 81-85.
    • Lim, D.-J., Anderson, T.R., Inman, O.L., 2014. Choosing effective dates from multiple optima in technology forecasting using data envelopment analysis (TFDEA). Technol. Forecast. Soc. Chang. 88, 91-97.
    • Makridakis, S., Hibon, M., 2000. The M3-competition: results, conclusions and implications. Int. J. Forecast. 16, 451-476.
    • Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, S., Winkler, R., 1982. The accuracy of extrapolation (time series) methods: results of a forecasting competition. J. Forecast. 1, 111-153.
    • Nakhaeizadeh, G., Schnabl, A., 1997. Development of Multi-Criteria Metrics for Evaluation of Data Mining Algorithms. KDD, pp. 37-42.
    • Nakhaeizadeh, G., Schnabl, A., 1998. Towards the Personalization of Algorithms Evaluation in Data Mining. KDD, pp. 289-293.
    • Pendharkar, P.C., Rodger, J.A., 2003. Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption. Decis. Support. Syst. 36, 117-136.
    • Seiford, L.M., Zhu, J., 2002. Modeling undesirable factors in efficiency evaluation. Eur. J. Oper. Res. 142, 16-20.
    • Syntetos, A.A., Boylan, J.E., 2005. The accuracy of intermittent demand estimates. Int. J. Forecast. 21, 303-314.
    • Witt, S.F., Witt, C.A., 1992. Modeling and Forecasting Demand in Tourism. Academic Press Ltd.
    • Xu, B., Ouenniche, J., 2011. A multidimensional framework for performance evaluation of forecasting models: context-dependent DEA. Appl. Financ. Econ. 21, 1873-1890.
    • Yokuma, J.T., Armstrong, J.S., 1995. Beyond accuracy: comparison of criteria used to select forecasting methods. Int. J. Forecast. 11, 591-597.
    • Ali Emrouznejad is a Professor and Chair in Business Analytics at Aston Business School, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as data mining. Dr. Emrouznejad is Editor of Annals of Operations Research, Associate Editor of Socio-Economic Planning Sciences, Associate Editor of IMA journal of Management Mathematics, Senior Editor of Data Envelopment Analysis journal, and member of editorial boards or guest editor in several other scientific journals. He has published over 80 articles in top ranked journals; he is author of the book on “Applied Operational Research with SAS”, editor of the books on “Performance Measurement with Fuzzy Data Envelopment Analysis”, “Managing Service Productivity” and “Handbook of Research on Strategic Performance Management and Measurement”. He is also co-founder of Performance Improvement Management Software (PIM-DEA), see http://www.DEAzone.com.
    • Bahman Rostami-Tabar is an assistant professor of operations and supply chain management in the School of Strategy and Leadership at Coventry University in the UK. In 2013, Bahman received the MIM best paper award (IFAC) and was also successful in Campus France Scholarship award (2009-2013). His research interests include Forecasting, data aggregation, information sharing and humanitarian supply chain management.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article