A novel weighted ensemble technique for time series forecasting

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7301 LNAI, Issue PART 1, 2012, Pages 38-49

  Adhikari, Ratnadip ^a   Agrawal, R K ^a  

^a JAWAHARLAL NEHRU UNIVERSITY   (India)

Author keywords

Artificial Neural Networks; Box Jenkins Models; Elman Networks; Ensemble Technique; Time Series Forecasting

Indexed keywords

BOX-JENKINS MODELS; COMBINATION WEIGHTS; COMBINING FORECASTS; ELMAN NETWORK; ENSEMBLE METHODS; ENSEMBLE TECHNIQUES; INDIVIDUAL MODELS; LINEAR METHODS; MULTIPLE TIME SERIES; TIME SERIES FORECASTING; VALIDATION APPROACH;

DATA MINING; ERROR STATISTICS; NEURAL NETWORKS; TIME SERIES;

FORECASTING;

EID: 84861452391     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-30217-6_4     Document Type: Conference Paper

References (20)

1. Gooijer, J.G., Hyndman, R.J.: 25 Years of time series forecasting. J. Forecasting 22(3), 443-473 (2006)
2. Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control, 3rd edn. Holden-Day, California (1970)
3. Armstrong, J.S.: Combining Forecasts. In: Armstrong, J.S. (ed.) Principles of Forecasting: A Handbook for Researchers and Practitioners. Kluwer Academic Publishers, Norwell (2001)
4. Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159-175 (2003)
5. Bates, J.M., Granger, C.W.J.: Combination of forecasts. Operational Research Quarterly 20(4), 451-468 (1969)
6. Clemen, R.T.: Combining forecasts: A review and annotated bibliography. J. Forecasting 5(4), 559-583 (1989)
7. Aksu, C., Gunter, S.: An empirical analysis of the accuracy of SA, OLS, ERLS and NRLS combination forecasts. J. Forecasting 8(1), 27-43 (1992)
8. Zou, H., Yang, Y.: Combining time series models for forecasting. J. Forecasting 20(1), 69-84 (2004)
9. Jose, V.R.R., Winkler, R.L.: Simple robust averages of forecasts: Some empirical results. International Journal of Forecasting 24(1), 163-169 (2008)
10. Lemke, C., Gabrys, B.: Meta-learning for time series forecasting and forecast combination. Neurocomputing 73, 2006-2016 (2010)
11. Bunn, D.: A Bayesian approach to the linear combination of forecasts. Operational Research Quarterly 26(2), 325-329 (1975)
12. Frietas, P.S., Rodrigues, A.J.: Model combination in neural-based forecasting. European Journal of Operational Research 173(3), 801-814 (2006)
13. Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with articial neural networks: The state of the art. J. Forecasting 14, 35-62 (1998)
14. Faraway, J., Chatfield, C.: Time series forecasting with neural networks: a comparative study using the airline data. J. Applied Statistics 47(2), 231-250 (1998)
15. Reidmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: The rprop algorithm. In: Proceedings of the IEEE Int. Conference on Neural Networks (ICNN), San Francisco, pp. 586-591 (1993)
16. Demuth, M., Beale, M., Hagan, M.: Neural Network Toolbox User's Guide. The MathWorks, Natic (2010)
17. Lim, C.P., Goh, W.Y.: The application of an ensemble of boosted Elman networks to time series prediction: A benchmark study. J. of Computational Intelligence 3, 119-126 (2005)
18. Time Series Data Library, http://robjhyndman.com/TSDL
19. Yahoo! Finance, http://finance.yahoo.com
20. Pacific FX database, http://fx.sauder.ubc.ca/data.html