Abstract
In this paper new algorithms with the combination between the Regional-Gradient-Guided Bootstrapping Algorithm and Dynamics Time Warping Technique for imputing incomplete time series data are proposed. The new measurement for curve similarity comparison by using the changing of slope of time series data are used. The main contribution of this paper is to propose new technique for imputing the fluctuate time series data. We compare our new method with Cubic interpolation, Multiple imputation, Windows Varies Similarity Measurement algorithms and Regional-Gradient-Guided Bootstrapping Algorithm. The experimental results showed that our new algorithms are outperform than these method.
This work is partially supported by a grant under The Commission on Higher Education Staff Development Project for the Joint Ph.D.Program in Computer Science at Chulalongkorn University, Thailand and one years research with Prof.Shigeru Mase at MASE Lab, Tokyo Institute of Technology, Japan.
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References
Chiewchanwattana, S., Lursinsap, C., Chu, C.H.: Imputing Incomplete Time Series Data Base on Varied-window Similarity Measure of Data Sequence. Pattern Recognition Letters 28, 1091–1113 (2007)
Wasito, I., Mirkin, B.: Nearest Neighbors Approach in the Least-squares Data Imputation Algorithms. Information Sciences 169, 1–25 (2005)
Wei, L., Keogh, E.: Semi-supervised Time Series Classifications. In: The 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 748–753. ACM Press, New York (2006)
Zoubir, A.M., Iskandes, D.R.: Bootstrap Methods and Application. IEEE Signal Processing Magazine 10, 1–10 (2007)
Wu, X., Barbara, D.: Modeling and Imputation of large incomplete Multidimensional Datasets. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 365–374. Springer, Heidelberg (2002)
Batista, G.E.A.P.A., Monard, M.C.: An Analysis of Four Missing Data Treatment Methods for Supervised Learning. Applied Artificial Intelligence 17, 519–533 (2003)
Zoubir, A.M., Iskandes, D.R.: Bootstrap Techniques for Signal Processing, 1st edn. Cambridge University Press, Cambridge (2004)
Prasomphan, S., Lursinsap, C., Chiewchanwattana, S.: Imputing Time Series Data by Regional-Gradient-Guided Bootstrapping Algorithm. In: The 9th International Symposium on Communications and Information Technology(ISCIT), pp. 163–168. IEEE Press, Incheon (2009)
Salvador, S., Chan, P.: Toward Accurate Dynamic Time Warping in Linear Time and Space. Intell. Data Anal. 11, 561–580 (2007)
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Prasomphan, S., Lursinsap, C., Chiewchanwattana, S. (2010). Two-Phase Imputation with Regional-Gradient-Guided Bootstrapping Algorithm and Dynamics Time Warping for Incomplete Time Series Data. In: Huang, DS., Zhang, X., Reyes GarcÃa, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_76
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DOI: https://doi.org/10.1007/978-3-642-14932-0_76
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