Abstract
In this paper we consider the problem of missing data in time series analysis. We propose a semi-supervised co-training method to handle the problem of missing data. We transform the time series data to set of labeled and unlabeled data. Different predictors are used to predict the unlabelled data and the most confident labeled patterns are used to retrain the predictors further to and enhance the overall prediction accuracy. By labeling the unknown patterns the missing data is compensated for. Experiments were conducted on different time series data and with varying percentage of missing data using a uniform distribution. We used KNN base predictors and Fuzzy Inductive Reasoning (FIR) base predictors and compared their performance using different confidence measures. Results reveal the effectiveness of the co-training method to compensate for the missing values and to improve prediction. The FIR model together with the ”similarity” confidence measures obtained in most cases the best results in our study.
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References
Tresp, V., Hofmann, R.: Missing and Noisy Data in Nonlinear Time-Series Prediction. In: Neural Networks for Signal Processing, pp. 1–10. IEEE Signal Processing Society, New York (1995)
Peter, J., Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting, 2nd edn. Springer Texts in Stat. Springer, Heidelberg (2002)
Lendasse, A., et al.: Vector quantization: a weighted version for timeseriesecasting. Future Generation Computer Systems 21, 1056–1067 (2005)
Cai, X., et al.: Time Series Prediction with Recurrent Neural Networks Using a Hybrid PSO-EA Algorithm. In: IJCNN’04, Budapest (2004)
Nguyen, H.H., Chan, C.W., Wilson, M.: Prediction Oil Well Production Using Multi-Neural Networks. In: Electrical and Computer Engineering, IEEE CCECE (2002)
Liu, X., Kwan, B.W., Foo, S.Y.: Time Series Prediction Based on Fuzzy Principles. Department of Electrical & Computer Engineering FAMU-FSU College of Engineering, Florida State University (2005)
Herrera, J.L.: Time Series Prediction Using Inductive Technique. Ph.D, instituto de organizacioa y control de sistemas industrials (1999)
Zhu, X.: Semi-supervised learning literature survey. Technical report, Computer Sciences TR 1530, Univ. Wisconsis, Madison, USA (Jan. 2006)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. of the Workshop on Computational Learning Theory, pp. 92–100 (1998)
Zhou, Y., Goldman, S.: Democratic Co-Learning. In: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), pp. 594–602 (2004)
Soliman, M., El Gayar, N.: A Co-training Approach for Semi-Supervised Multiple Classifiers. ICGST International Journal on Artificial Intelligence and Machine Learning, ICGSTAIML(Special Issue on Multiple Classifier Systems) 6, 9–16 (2006)
El Gayar, N., Shaban, S.A., Hamdy, S.: Face Recognition with co-training and ensemble driven learning. WSEAS Transactions on Computers 6(3), 507–513 (2007)
Didaci, L., Roli, F.: Using Co-training and Self-training in Semi-supervised Multiple Classifier Systems. In: Yeung, D.-Y., et al. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 522–530. Springer, Heidelberg (2006)
Zhou, Z.H., Li, M.: Semi-supervised regression with co-training. In: International Joint Conference on Artificial Intelligence (IJCAI) (2005)
Dasarthy, B.V.: Nearest Neighbor Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos (1991)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley and Sons, New York (2001)
Lendasse, A., et al.: Time Series Prediction Competition: The CATS Benchmark. In: IJCNN’2004 proceedings, Budapest, Hungary, 25-29 July 2004, pp. 1615–1620. IEEE, Los Alamitos (2004)
Atiya, A.F., et al.: A Comparison Between Neural-Network Forecasting Techniques–Case Study: River Flow Forecasting. IEEE Transactions on Neural Networks 10(2) (1999)
Fitzek, F.H.P., Reisslein, M.: MPEG-4 and H:263 video traces for network performance evaluation. IEEE Network 15(6), 40–54 (2001)
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Mohamed, T.A., El Gayar, N., Atiya, A.F. (2007). A Co-training Approach for Time Series Prediction with Missing Data. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_10
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DOI: https://doi.org/10.1007/978-3-540-72523-7_10
Publisher Name: Springer, Berlin, Heidelberg
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