Abstract
There is no individual forecasting method that is generally for any given time series better than any other method. Thus, no matter the efficiency of a chosen method, there always exists a danger that for a given time series the chosen method is inappropriate. To overcome such a problem and avoid the above mentioned danger, distinct ensemble techniques that combine more individual forecasting methods are designed. These techniques basically construct a forecast as a linear combination of forecasts by individual methods. In this contribution, we construct a novel ensemble technique that determines the weights based on time series features. The protocol that carries a knowledge how to combine the individual forecasts is a fuzzy rule base (linguistic description). An exhaustive experimental justification is provided. The suggested ensemble approach based on fuzzy rules demonstrates both, lower forecasting error and higher robustness.
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References
Adya, M., Armstrong, J.S., Collopy, F., Kennedy, M.: An application of rule-based forecasting to a situation lacking domain knowledge. Int. J. Forecasting 16, 477–484 (2000)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of 20th Int. Conf. on Very Large Databases, pp. 487–499. AAAI Press (1994)
Armstrong, J.S.: Evaluating methods. In: Principles of Forecasting: A Handbook for Reasearchers and Practitioners. Kluwer, Dordrecht (2001)
Armstrong, J.S., Collopy, F.: Error measures for generalizing about forecasting methods: Empirical comparisons. Int. J. Forecasting 8, 69–80 (1992)
Armstrong, J.S., Adya, M., Collopy, F.: Rule-Based Forecasting Using Judgment in Time Series Extrapolation. In: Principles of Forecasting: A Handbook for Reasearchers and Practitioners. Kluwer, Dordrecht (2001)
Barrow, D.K., Crone, S.F., Kourentzes, N.: An Evaluation of Neural Network Ensembles and Model Selection for Time Series Prediction. In: Proc. of 2010 IEEE IJCNN. IEEE Press, Barcelona (2010)
Bates, J.M., Granger, C.W.J.: Combination of Forecasts. Oper. Res. Q 20, 451–468 (1969)
Bělohlávek, R., Novák, V.: Learning Rule Base of the Linguistic Expert Systems. Soft Comput. 7, 79–88 (2002)
Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1976)
Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E., Winkler, R.: The Accuracy of Extrapolation (Time-Series) Methods — Results of a Forecasting Competition. J. Forecasting 1, 111–153 (1982)
Collopy, F., Armstrong, J.S.: Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations. Manage. Sci. 38, 1394–1414 (1992)
Cortez, P., Rocha, M., Neves, J.: Evolving Time Series Forecasting ARMA Models. J. Heuristics 10, 415–429 (2004)
Crone, S.F., Hibon, M., Nikolopoulos, K.: Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. Int. J. Forecasting 27, 635–660 (2011)
Dickey, D.A., Fuller, W.A.: Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74, 427–431 (1979)
Dvořák, A., Štěpnička, M., Vavříčková, L.: Redundancies in systems of fuzzy/linguistic IF-THEN rules. In: Proc. EUSFLAT 2011, pp. 1022–1029 (2011)
Hamilton, J.D.: Time Series Analysis. Princeton University Press (1994)
Hájek, P.: The question of a general concept of the GUHA method. Kybernetika 4, 505–515 (1968)
Hájek, P., Havránek, T.: Mechanizing hypothesis formation: Mathematical foundations for a general theory. Springer, Berlin (1978)
Hyndman, R., Koehler, A.: Another look at measures of forecast accuracy. Int. J. Forecasting 22, 679–688 (2006)
Kupka, J., Tomanová, I.: Some extensions of mining of linguistic associations. Neural Netw. World 20, 27–44 (2010)
Lemke, C., Gabrys, B.: Meta-learning for time series forecasting in the NN GC1 competition. In: Proc. of 2010 FUZZ-IEEE, Barcelona, Spain. IEEE Press (2010)
MacKinnon, J.G.: Numerical Distribution Functions for Unit Root and Cointegration Tests. J. Appl. Econom. 11, 601–618 (1996)
Makridakis, S., Hibon, M.: The M3-Competition: Results, Conclusions and Implications. Int. J. Forecasting 16, 451–476 (2000)
Makridakis, S., Wheelwright, S., Hyndman, R.: Forecasting methods and applications, 3rd edn. John Wiley & Sons (2008)
Newbold, P., Granger, C.W.J.: Experience with forecasting univariate time series and combination of forecasts. J. Roy. Stat. Soc. A Sta. 137, 131–165 (1974)
Novák, V.: A comprehensive theory of trichotomous evaluative linguistic expressions. Fuzzy Set. Syst. 159, 2939–2969 (2008)
Novák, V.: Perception-based logical deduction. In: Computational Intelligence, Theory and Applications. ASC, pp. 237–250. Springer, Berlin (2005)
Novák, V., Perfilieva, I., Dvořák, A., Chen, Q., Wei, Q., Yan, P.: Mining pure linguistic associations from numerical data. Int. J. Approx. Reason. 48, 4–22 (2008)
Štěpnička, M., Donate, J.P., Cortez, P., Vavříčková, L., Gutierrez, G.: Forecasting seasonal time series with computational intelligence: contribution of a combination of distinct methods. In: Proc. of EUSFLAT 2011, pp. 464–471 (2011)
Štěpnička, M., Dvořák, A., Pavliska, V., Vavříčková, L.: A linguistic approach to time series modeling with the help of the F-transform. Fuzzy Set. Syst. 180, 164–184 (2011)
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Sikora, D., Štěpnička, M., Vavříčková, L. (2013). Fuzzy Rule-Based Ensemble Forecasting: Introductory Study. In: Kruse, R., Berthold, M., Moewes, C., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_41
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DOI: https://doi.org/10.1007/978-3-642-33042-1_41
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