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2017, vol. 12, iss. 2, pp. 217-236
A new consensus between the mean and median combination methods to improve forecasting accuracy
Dokuz Eylul University, Department of Econometrics, İzmir, Turkey
Keywords: forecasting; time series; combined forecasts; least support vector machines; neural networks; SETAR; LSTAR; ARIMA
To improve the forecasting accuracies, researchers have long been using various combination techniques. In particular, the use of dissimilar methods for forecasting time series data is expected to provide superior results. Although numerous combination techniques have been proposed until date, the simple combination techniques - such as mean and median - maintain their strength, popularity, and utility. This paper proposes a new combination method based on the mean and median combination methods so as to combine the advantages of both these methods. The proposed combination technique attempts to utilize the strong aspects of each method and minimize the risk that arises from the selection of the combination method with poor performance. In order to depict the potential power of the proposed combining method, well-known six real-world time series data were used. Our results indicate that the proposed method presents with promising performances. In addition, a nonparametric statistical test was exploited to reveal the superiority of the proposed method over the single methods and other forecast combination methods from all of the investigated data sets.
Adhikari, R., Agrawal, R. K. (2014) A linear hybrid methodology for improving accuracy of time series forecasting. Neural Computing and Applications, 25(2): 269-281
Agnew, C.E. (1985) Bayesian consensus forecasts of macroeconomic variables. Journal of Forecasting, 4(4): 363-376
Armstrong, J.S. (2001) Combining forecasts. in: Armstrong J.S. [ed.] Principles of forecasting: A handbook for researchers and practitioners, Norwell, MA: Kluwer Academic Publishers, Chapter 13
Armstrong, J. (1989) Combining forecasts: The end of the beginning or the beginning of the end?. International Journal of Forecasting, 5(4): 585-588
Bacon, D.W., Watts, D.G. (1971) Estimating the transition between two intersecting straight lines. Biometrika, 58(3): 525-534
Bates, J. M., Granger, C. W. J. (1969) The Combination of Forecasts. OR, 20(4): 451
Box, G.E.P., Jenkins, G.M. (1970) Time series analysis: Forecasting and control. San Francisco: Holden Day
Box, G.E.P., Jenkins, G.M. (1976) Time series analysis: Forecasting and control. San Francisco, CA: Holden Day
Bunn, D. W. (1975) A Bayesian Approach to the Linear Combination of Forecasts. Operational Research Quarterly, 26(2): 325
Cancelo, J.R., Mourelle, E. (2005) Modeling Cyclical Asymmetries in GDP: International Evidence. Atlantic Economic Journal, 33(3): 297-309
Chan, W., Wong, A.C. S., Tong, H. (2004) Some Nonlinear Threshold Autoregressive Time Series Models for Actuarial Use. North American Actuarial Journal, 8(4): 37-61
Chan, Y.L., Stock, J.H., Watson, M.W. (1999) A dynamic factor model framework for forecast combination. Spanish Economic Review, 1(2): 91-121
Chih-Wei, H., Chih-Jen, L. (2002) A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13(2): 415-425
Clemen, R.T. (1989) Combining forecasts: A review and annotated bibliography. Int J Forecast, 5, str. 559-584
de Gooijer, J.G., Hyndman, R.J. (2006) 25 years of time series forecasting. International Journal of Forecasting, 22(3): 443-473
de Groot, C., Würtz, D. (1991) Analysis of univariate time series with connectionist nets: A case study of two classical examples. Neurocomputing, 3(4): 177-192
de Menezes, L.M., Bunn, D., Taylor, J.W. (2000) Review of guidelines for the use of combined forecasts. European Journal of Operational Research, 120(1): 190-204
Deutsch, M., Granger, C.W.J., Teräsvirta, T. (1994) The combination of forecasts using changing weights. International Journal of Forecasting, 10(1): 47-57
Diebold, F.X., Pauly, P. (1990) The use of prior information in forecast combination. International Journal of Forecasting, 6(4): 503-508
Dijk, D.van, Teräsvirta, T., Franses, P.H. (2002) Smooth transition autoregressive models - a survey of recent developments. Econometric Reviews, 21(1): 1-47
Faraway, J., Chatfield, C. (2008) Time series forecasting with neural networks: a comparative study using the air line data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(2): 231-250
Feng, H., Liu, J. (2003) A SETAR model for Canadian GDP: non-linearities and forecast comparisons. Applied Economics, 35(18): 1957-1964
Fiordaliso, A. (1998) A nonlinear forecasts combination method based on Takagi-Sugeno fuzzy systems. International Journal of Forecasting, 14(3): 367-379
Franses, P.H., Dijk, D.V., Opschoor, A. (2014) Time Series Models For Business and Economic Forecasting. New York, NY: Cambridge University Press
Genre, V., Kenny, G., Meyler, A., Timmermann, A. (2013) Combining expert forecasts: Can anything beat the simple average?. International Journal of Forecasting, 29(1): 108-121
Graefe, A., Armstrong, J. S., Jones, R.J., Cuzán, A.G. (2014) Combining forecasts: An application to elections. International Journal of Forecasting, 30(1): 43-54
Hagan, M.T., Demuth, H.B., Beale, M. (1996) Neural Network Design. Boston: PWS
Hansen, J.V., McDonald, J.B., Nelson, R.D. (1999) Time Series Prediction With Genetic-Algorithm Designed Neural Networks: An Empirical Comparison With Modern Statistical Models. Computational Intelligence, 15(3): 171-184
Hassan, S., Khosravi, A., Jaafar, J., Belhaouari, S.B. (2012) Load Forecasting Accuracy through Combination of Trimmed Forecasts. Berlin, Heidelberg: Springer Nature, str. 152-159
Hibon, M., Evgeniou, T. (2005) To combine or not to combine: Selecting among forecasts and their combinations. International Journal of Forecasting, 21(1): 15-24
Hipel, K.W., McLeod, A.I. (1994) Time series modelling of water resources and environmental systems. Elsevier, Vol. 45
Hochberg, Y., Tamhane, A.C. (1987) Multiple Comparison Procedures. Hoboken, NJ: John Wiley & Sons
Hyndman, R.J. (2012) Time series data library. (12/06/2012)
Jose, V.R.R., Winkler, R.L. (2008) Simple robust averages of forecasts: Some empirical results. International Journal of Forecasting, 24(1): 163-169
Larreche, J., Moinpour, R. (1983) Managerial Judgment in Marketing: The Concept of Expertise. Journal of Marketing Research, 20(2): 110
Larrick, R., Soll, J. (2003) Intuitions about combining opinions: Misappreciation of the averaging principle. Working paper INSEAD, 09/TM
Lemke, C., Gabrys, B. (2010) Meta-learning for time series forecasting and forecast combination. Neurocomputing, 73(10-12): 2006-2016
Makridakis, S., Winkler, R.L. (1983) Averages of forecasts: Some empirical results. Management Science, 29, 9, 987-996
Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E., Winkler, R. (1982) The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1(2): 111-153
Makridakis, S., Wheelwright, S.C., Hyndman, R.J. (1998) Forecasting: Methods and applications. New York, itd: Wiley
Marcellino, M. (2004) Forecast Pooling for European Macroeconomic Variables*. Oxford Bulletin of Economics and Statistics, 66(1): 91-112
McNees, S.K. (1992) The uses and abuses of 'consensus' forecasts. Journal of Forecasting, 11(8): 703-710
Miller, C.M., Clemen, R.T., Winkler, R.L. (1992) The effect of nonstationarity on combined forecasts. International Journal of Forecasting, 7(4): 515-529
Newbold, P., Granger, C.W. (1974) Experience with forecasting univariate time series and the combination of forecasts. Journal of the Royal Statistical Society, Ser. A, 137, 131-164
Newbold, P., Harvey, D.I. (2002) Forecast combination and encompassing. in: Clements M.P.; Hendry D.F. [ed.] A Companion to Economic Forecasting, Oxford: Blackwell, Pp. 268-283
Priestley, M.B. (1988) Non-linear and Non-Stationary Time Series Analysis. San Diego, CA: Academic Press
Rao, S.T., Sabr, M.M. (1984) An Introduction to Bispectral Analysis and Bilinear Time Series Models. New York: Springer-Verlag
Rapach, D.E., Strauss, J.K., Zhou, G. (2010) Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy. Review of Financial Studies, 23 (2); 821-862
Reid, D.J. (1968) Combining Three Estimates of Gross Domestic Product. Economica, 35(140): 431
Schauberger, G., Tutz, G. (2014) Regularization methods in economic forecasting. in: Empirical Economic and Financial Research: Theory, Methods and Practice (Advanced Studies in Theoretical and Applied Econometrics), Switzerland: Springer International Publishing, pp. 61-80
Smith, J., Wallis, K.F. (2009) A Simple Explanation of the Forecast Combination Puzzle. Oxford Bulletin of Economics and Statistics, 71(3): 331-355
Stock, J.H., Watson, M. (1999) A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series. in: Engle R.F.; White H. [ed.] Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W.J. Granger, Oxford, UK: Oxford University Press, Pp. 1-44
Stock, J.H., Watson, M.W. (1999) Forecasting inflation. Journal of Monetary Economics, 44(2): 293-335
Stock, J.H., Watson, M.W. (2004) Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23(6): 405-430
Suykens, J.A.K., de Brabanter, J., Lukas, L., Vandewalle, J. (2002) Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48(1-4): 85-105
Terasvirta, T., Anderson, H. M. (1992) Characterizing nonlinearities in business cycles using smooth transition autoregressive models. Journal of Applied Econometrics, 7(S1): S119-S136
Terui, N., van Dijk, H.K. (2002) Combined forecasts from linear and nonlinear time series models. International Journal of Forecasting, 18(3): 421-438
Timmermann, A. (2006) Forecast combinations. in: Elliott G.; Granger C.; Timmermann A. [ed.] Handbook of Economic Forecasting, Elsevier, Pp. 135-196
Tong, H. (1978) On a Threshold Model. in: Chen, C. H. [ed.] Pattern Recognition and Signal Processing, Dordrecht: Springer Nature, str. 575-586
Velleman, P.F., Hoaglin, D.C. (1981) Applications, Basics and Computing of Exploratory Data Analysis. Boston: Duxbury Press
Winkler, R.L., Clemen, R.T. (1992) Sensitivity of Weights in Combining Forecasts. Operations Research, 40(3): 609-614
Woodward, W.A., Gray, H.L., Elliott, A.C. (2011) Applied Time Series Analysis. London, UK: CRC Press
Zhang, G. (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50: 159-175
Zhang, G., Eddy, P.B., Hu, M. (1998) Forecasting with artificial neural networks:. International Journal of Forecasting, 14(1): 35-62
Zhang, G.P. (2001) An investigation of neural networks for linear time-series forecasting. Computers & Operations Research, 28(12): 1183-1202
Zou, H.F., Xia, G.P., Yang, F.T., Wang, H.Y. (2007) An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing, 70(16-18): 2913-2923
Zou, H., Yang, Y. (2004) Combining time series models for forecasting. International Journal of Forecasting, 20(1): 69-84


article language: English
document type: Original Paper
DOI: 10.5937/sjm12-13091
published in SCIndeks: 02/11/2017
peer review method: double-blind
Creative Commons License 4.0

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