This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
A New Temporal Pattern Identification Method for Characterization and Prediction of Complex Time Series Events
March/April 2003 (vol. 15 no. 2)
pp. 339-352

Abstract—A new method for analyzing time series data is introduced in this paper. Inspired by data mining, the new method employs time-delayed embedding and identifies temporal patterns in the resulting phase spaces. An optimization method is applied to search the phase spaces for optimal heterogeneous temporal pattern clusters that reveal hidden temporal patterns, which are characteristic and predictive of time series events. The fundemantal concepts and framework of the method are explained in detail. The method is then applied to the characterization and prediction, with a high degree of accuracy, of the release of metal droplets from a welder. The results of the method are compared to those from a Time Delay Neural Network and the C4.5 decision tree algorithm.

[1] S.M. Pandit and S.-M. Wu, Time Series and System Analysis, with Applications. New York: Wiley, 1983.
[2] S. Weiss and N. Indurkhya, Predictive Data Mining: A Practical Guide, Morgan Kaufmann, San Francisco, 1998.
[3] U. Fayyad et al., eds., Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, Mass., 1996.
[4] P. Cabena and International Business Machines Corporation, Discovering Data Mining: From Concept to Implementation. Upper Saddle River, NJ: Prentice Hall, 1998.
[5] D. Berndt and J. Clifford, "Finding Patterns in Time Series: A Dynamic Programming Approach," Advances in Knowledge Discovery and Data Mining, U.M. Fayyad et al., eds., AAAI Press, Menlo Park, Calif., 1996, pp. 229-247.
[6] E. Keogh and P. Smyth, “A Probabilistic Approach to Fast Pattern Matching in Time Series Databases,” Proc. Third Int'l Conf. Knowledge Discovery and Data Mining, 1997.
[7] E. Keogh, “A Fast and Robust Method for Pattern Matching in Time Series Databases,” Proc. Ninth Int'l Conf. Tools with Artificial Intelligence (TAI '97), 1997.
[8] E.J. Keogh and M.J. Pazzani, “An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback,” Proc. AAAI Workshop Predicting the Future: AI Approaches to Time-Series Analysis, 1998.
[9] M.T. Rosenstein and P.R. Cohen, “Continuous Categories For a Mobile Robot,” Proc. 16th National Conf. Artificial Intelligence, 1999.
[10] V. Guralnik, D. Wijesekera, and J. Srivastava, “Pattern Directed Mining of Sequence Data,” Proc. Int'l Conf. Knowledge Discovery and Data Mining, pp. 51-57, 1998.
[11] C. Faloutsos, M. Ranganathan, and I. Manolopoulos, “Fast Subsequence Matching in Time Series Databases,” Proc. ACM SIGMOD, pp. 419-429, May 1994.
[12] B.-K. Yi, H.V. Jagadish, and C. Faloutsos, “Efficient Retrieval of Similar Time Sequences under Time Warping,” Proc. Int'l Conf. Data Eng., 1998.
[13] R. Agrawal, C. Faloutsos, and A. Swami, “Efficient Similarity Search in Sequence Databases,” Proc. Fourth Int'l Conf. Foundations of Data Organization and Algorithms, pp. 69-84, Oct. 1993.
[14] C. Faloutsos, H.V. Jagadish, A.O. Mendelzon, and T. Milo, “A Signature Technique for Similarity-Based Queries,” Proc. Compression and Complexity of Sequences (SEQUENCES '97), June 1997.
[15] H.D.I. Abarbanel, Analysis of Observed Chaotic Data. New York: Springer, 1996.
[16] T. Sauer, J.A. Yorke, and M. Casdagli, “Embedology,” J. Statistical Physics, vol. 65, pp. 579-616, 1991.
[17] R.J. Povinelli and X. Feng, “Temporal Pattern Identification of Time Series Data using Pattern Wavelets and Genetic Algorithms,” Proc. Artificial Neural Networks in Eng. Conf., pp. 691-696, 1998.
[18] H.D.I. Abarbanel, R. Brown, J.J. Sidorowich, and L.S. Tsimring, “The Analysis of Observed Chaotic Data in Physical Systems,” Rev. Modern Physics, vol. 65, pp. 1331-1392, 1993.
[19] J. Iwanski and E. Bradley, “Recurrence Plot Analysis: To Embed or not to Embed?,” Chaos, vol. 8, pp. 861-871, 1998.
[20] J.R. Quinlan, C4.5: Programs for Machine Learning,San Mateo, Calif.: Morgan Kaufman, 1992.
[21] C.-T. Lin and C.S.G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent System.Upper Saddle River, N.J.: Prentice Hall, 1996.
[22] F. Takens, “Detecting Strange Attractors in Turbulence,” Proc. Dynamical Systems and Turbulence, pp. 366-381, 1980.
[23] E. Bradley, “Analysis of Time Series,” An Introduction to Intelligent Data Analysis, M. Berthold and D. Hand, eds., pp. 167-194, New York: Springer, 1999.
[24] R.J. Povinelli and X. Feng, “Data Mining of Multiple Nonstationary Time Series,” Proc. Artificial Neural Networks in Eng., pp. 511-516, 1999.
[25] D.J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures. Boca Raton, Fla.: CRC Press, 1997.
[26] D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass.: Addison-Wesley, 1989.
[27] R.J. Povinelli and X. Feng, “Improving Genetic Algorithms Performance By Hashing Fitness Values,” Proc. Artificial Neural Networks in Eng. Conf., pp. 399-404, 1999.
[28] R.J. Povinelli, “Improving Computational Performance of Genetic Algorithms: A Comparison of Techniques,” Proc. Genetic and Evolutionary Computation Conf. (GECCO-2000) Late Breaking Papers, pp. 297-302, 2000.
[29] D.G. Luenberger, Optimization by Vector Space Methods. New York: John Wiley&Sons, 1969.
[30] R.J. Povinelli, “Time Series Data Mining: Identifying Temporal Patterns for Characterization and Prediction of Time Series Events,” PhD dissertation, Marquette Univ., 1999.
[31] R.J. Povinelli, “Identifying Temporal Patterns for Characterization and Prediction of Financial Time Series Events,” Proc. Temporal, Spatial and Spatio-Temporal Data Mining: First Int'l Workshop; revised papers, (TSDM '00), pp. 46-61, 2000.

Index Terms:
Temporal pattern identification, time series analysis, data mining, time delay embedding, optimization clustering, and genetic algorithms.
Citation:
Richard J. Povinelli, Xin Feng, "A New Temporal Pattern Identification Method for Characterization and Prediction of Complex Time Series Events," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 2, pp. 339-352, March-April 2003, doi:10.1109/TKDE.2003.1185838
Usage of this product signifies your acceptance of the Terms of Use.