Prediction of composite indicators using combined method of extreme learning machine and locally weighted regression

Nonlinear Analysis: Modelling and Control

Volumn 17, Issue 2, 2012, Pages 238-251

  Rukšenaite, Jurga ^a   Vaitkus, Pranas ^b  

^a VILNIUS GEDIMINAS TECHNICAL UNIVERSITY   (Lithuania)

^b VILNIUS UNIVERSITY   (Lithuania)

Author keywords

Composite indicators; ELM; Locally weighted regression; Neural networks

Indexed keywords

EID: 84861703608     PISSN: 13925113     EISSN: 23358963     Source Type: Journal    
DOI: 10.15388/na.17.2.14071     Document Type: Article

References (33)

1. R. Bandura, A survey of composite indices measuring country performance: 2008 update, A UNDP/ODS working paper, 2008.
2. J.E. Stiglitz, A. Sen, J.P. Fitoussi, Report by the Commission on the Measurement of Economic Performance and Social Progress, 2009, www.stiglitz-sen-fitoussi.fr.
3. J. Rukšenaite, Impact of factor rotation methods on simulation composite indicators, Math. Model. Anal., 16(3), pp. 418-431, 2011.
4. Statistics Lithuania, www.stat.gov.lt.
5. The Bank of Lithuania, www.lb.lt.
6. G. Rünstler, K. Barhoumi, S. Benk, R. Cristadoro, A. Den Reijer, A. Jakaitiene, P. Jelonek, A. Rua, K. Ruth, C. Van Nieuwenhuyze, Short-term forecasting of GDP using large monthly data sets: A pseudo real-time forecast evaluation exercise, Lietuvos bankas working paper, No. 1, 2008.
7. G.E.P. Box, G.M. Jenkins, G.C. Reinsel, Time Series Analysis: Forecasting and Control, Prentice Hall, Englewood Cliffs, NJ, 1994.
8. S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd edition, Prentice Hall, New York, 1999.
9. F. Takens, Detecting strange attractors in turbulence, in: Dynamical Systems and Turbulence, Warwick 1980, Lect Notes Math., Vol. 898, Springer, Heidelberg, 1981, pp. 366-381.
10. A. Lendasse, E. De Bodt, V. Wertz, M. Verleysen, Non-linear financial time series forecasting Application to the Bel 20 stock market index, Eur. J. Econ. Soc. Syst., 14(1), pp. 81-91, 2000.
11. G. Tkacz, Neural network forecasting of Canadian GDP growth, Int. J. Forecasting, 17, pp. 57-69, 2001.
12. P. McNelis, P. McAdam, Forecasting inflation with thick models and neural networks, European Central Bank working paper, No. 352, 2004.
13. A. Jakaitiene, M. Tamošiunaite, Lithuanian economic growth parameters prediction using neural networks, Lith. Math. J., 43, pp. 635-639, 2003.
14. P. Zhang, D.M. Kline, Quarterly time-series forecasting with neural networks, IEEE Trans. Neural Networks, 18, pp. 1800-1814, 2007.
15. L. Barbone, G. Bodo, I. Visco, Costi e profitti in senso stretto: un'analisi su serie trimestrali, 1970-1980, Bollettino della Banca d'Italia, 36, pp. 465-510, 1981.
16. F.T. Denton, Adjustment of monthly or quarterly series to annual totals: An approach based on quadratic minimization, J. Am. Stat. Assoc., 66, pp. 99-102, 1971.
17. R. Kaiser, A. Maravall, An application of TRAMO-SEATS: Changes in seasonality and current trend-cycle assessment: The German retail trade turnover series, Banco de Espana working paper, No. 0011, 2000.
18. S.A. Aivazian, B.S. Mchitarian, Applied Statistics and Base of Econometrics, State University Higher School of Economics, Moscow, 1998 (in Russian).
19. J.O. Kim, C.W. Muller et al., Factor, Discriminant and Cluster Analysis, Finansy i statistika, Moscow, 1989 (in Russian).
20. G. Nicoletti, S. Scarpetta, O. Boylaud, Summary indicators of product market regulation with an extension to employment protection legislation, OECD Economics Department working papers, No. 226, 2000.
21. M.T. Hagan, M. Menhaj, Training feed-forward networks with the Marquardt algorithm, IEEE Trans. Neural Networks, 5, pp. 989-993, 1994.
22. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: Theory and applications, Neurocomputing, 70, pp. 489-501, 2006.
23. D. Nguyen, B. Widrow, Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights, in: Proceedings of the International Joint Conference on Neural Networks, Vol. 3, 1990, pp. 21-26.
24. G.-B. Huang, D. Wang, Y. Lana, Extreme learning machines: A survey, Int. J. Mach. Learn. & Cybern., 2, pp. 107-122, 2011.
25. P.L. Bartlett, The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network, IEEE Trans. Inf. Theory, 44, pp. 525-536, 1998.
26. C.G. Atkeson, A.W. Moore, S. Schaal, Locally weighted learning, Artif. Intell. Rev., 11, pp. 11-73, 1997.
27. R. Baronas, F. Ivanauskas, R. Maslovskis, M. Radavičius, P. Vaitkus, Locally weighted neural networks for analysis of the biosensor response, Kybernetika, 43, pp. 21-30, 2007.
28. A.W. Moore, J.G. Schneider, K. Deng, Efficient locally weighted polynomial regression predictions, in: Proc. Fourteenth International Conference on Machine Learning, 1997, pp. 236-244.
29. D. Ruppert, M.P. Wand, Multivariate locally weighted least squares regression, Ann. Stat., 22, pp. 1346-1370, 1994.
30. R. Sitte, J. Sitte, Neural networks approach to the random walk dilemma of financial time series, Appl. Intell., 16, pp. 163-171, 2002.
31. K.J. Lang, G.E. Hinton, The development of the time-delay neural network architecture for speech recognition, Carnegie-Mellon University, CMU-CS-88-152, 1988.
32. A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, K.J. Lang, Phoneme recognition using timedelay neural networks, IEEE Trans. Acoust. Speech Signal Process., 37, pp. 328-339, 1989.
33. J.L. Elman, Finding structure in time, Cognitive Sci., 14, pp. 179-211, 1990.