Abstract
Existing methods for time series clustering rely on the actual data values can become impractical since the methods do not easily handle dataset with high dimensionality, missing value, or different lengths. In this paper, a dimension reduction method is proposed that replaces the raw data with some global measures of time series characteristics. These measures are then clustered using a self-organizing map. The proposed approach has been tested using benchmark time series previously reported for time series clustering, and is shown to yield useful and robust clustering.
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Bradley, P.S., Fayyad, U.M.: Refining Initial Points for K-means Clustering. In: The 15th international conference on machine learning, Madison (1998)
Keogh, E., Lin, J., Truppel, W.: Clustering of Time Series Subsequences is Meaningless: Implications for Past and Future Research. In: The 3rd IEEE International Conference on Data Mining, Melbourne
Wang, C., Wang, X.S.: Supporting Content-based Searches on Time Series via Approximation. In: The 12th international conference on scientific and statistical database management, Berlin (2000)
Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting Methods and Applications, 3rd edn. John Wiley & Sons, Inc., Chichester (1998)
Harvill, J.L., Ray, B.K., Harvill, J.L.: Testing for Nonlinearity in a Vector Time Series. Biometrika 86, 728–734 (1999)
Blake, A.P., Kapetanios, G.: A Radial Basis Function Artificial Neural Network Test for Neglected Nonlinearity. The Econometrics Journal 6(2), 357–373 (2003)
Haslett, J., Raftery, A.E.: Space-Time Modelling with Long-Memory Dependence: Assessing Ireland’s Wind Power Resource (With Discussion). Applied Statistics 38, 1–50 (1989)
Hilborn, R.C.: Chaos and Nonlinear Dynamics: an Introduction for Scientists and Engineers. Oxford University Press, New York (1994)
Kohonen, T.: Self-Organizing Maps, vol. 30. Springer, Heidelberg (1995)
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Wang, X., Smith, K.A., Hyndman, R.J. (2005). Dimension Reduction for Clustering Time Series Using Global Characteristics. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3516. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428862_108
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DOI: https://doi.org/10.1007/11428862_108
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