A novel ranking procedure for forecasting approaches using Data Envelopment Analysis

Article English OPEN
Emrouznejad, Ali ; Rostami-Tabar, Bahman ; Petridis, Konstantinos (2016)
  • Publisher: Elsevier BV
  • Journal: Technological Forecasting and Social Change, volume 111, pages 235-243 (issn: 0040-1625)
  • Related identifiers: doi: 10.1016/j.techfore.2016.07.004
  • Subject: 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.
  • References (39)
    39 references, page 1 of 4

    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.

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