Abstract
The Sigma-Point Kalman Filters (SPKF) is a family of filters that achieve very good performance when applied to time series. Currently most researches involving time series forecasting use the Sigma-Point Kalman Filters, however they do not use an ensemble of them, which could achieve a better performance. The REC analysis is a powerful technique for visualization and comparison of regression models. The objective of this work is to advocate the use of REC curves in order to compare the SPKF and ensembles of them and select the best model to be used.
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References
Bi, J., Bennett, K.P.: Regression Error Characteristic Curves. In: Proceedings of the 20th International Conference on Machine Learning (ICML), Washington, DC, pp. 43-50 (2003)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. Machinereadable data repository, University of California, Department of Information and Computer Science, Irvine, CA (2005), http://www.ics.uci.edu/~mlearn/MLRepository.html
Caruana, R., Niculescu-Mizil, A.: An Empirical Evaluation of Supervised Learning for ROC Area. In: Proceedings of the First Workshop on ROC Analysis (ROCAI), pp. 1-8 (2004)
Dietterich, T.G.: Machine Learning Research: Four Current Directions. The AI Magazine 18, 97–136 (1998)
Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte-Carlo Methods in Practice. Springer, Heidelberg (2001)
Dzeroski, S., Zenko, B.: Is Combining Classifiers with Stacking Better than Selecting the Best One? Machine Learning 54, 255–273 (2004)
Frank, E., Trigg, L., Holmes, G., Witten, I.H.: Naive Bayes for Regression. Machine Learning 41, 5–25 (2000)
Ito, K., Xiong, K.: Gaussian Filters for Nonlinear Filtering Problems. IEEE Transactions on Automatic Control 45, 910–927 (2000)
van der Merwe, R., Wan, E.: Efficient Derivative-Free Kalman Filters for Online Learning. In: Proceedings of the 9th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium (2001)
van der Merwe, R., Wan, E.: Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models. In: Proceedings of the Workshop on Advances in Machine Learning, Montreal, Canada (2003)
Jazwinsky, A.: Stochastic Processes and Filtering Theory. Academic Press, New York (1970)
Julier, S., Uhlmann, J., Durrant-Whyte, H.: A New Approach for Filtering Nonlinear Systems. In: Proceedings of the American Control Conference, pp. 1628–1632 (1995)
Keogh, E., Folias, T.: The UCR Time Series Data Mining Archive. University of California, Computer Science & Engineering Department, Riverside, CA (2002), http://www.cs.ucr.edu/~eamonn/TSDMA/index.html
Provost, F., Fawcett, T.: Analysis and Visualization of Classifier Performance: Comparison Under Imprecise Class and Cost Distributions. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD), pp. 43–48. AAAI Press, Menlo Park (1997)
Provost, F., Fawcett, T., Kohavi, R.: The Case Against Accuracy Estimation for Comparing Classifiers. In: Proceedings of the 15th International Conference on Machine Learning (ICML), pp. 445–453. Morgan Kaufmann, San Francisco (1998)
Quinlan, J.R.: Learning with Continuous Classes. In: Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, pp. 343–348. World Scientific, Singapore (1992)
Teixeira, M., Zaverucha, G.: Fuzzy Bayes and Fuzzy Markov Predictors. Journal of Intelligent and Fuzzy Systems 13, 155–165 (2003)
Wolpert, D.: Stacked generalization. Neural Networks 5, 241–260 (1992)
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de Pina, A.C., Zaverucha, G. (2006). Applying REC Analysis to Ensembles of Sigma-Point Kalman Filters. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_16
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DOI: https://doi.org/10.1007/11840930_16
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