A. Kidron∗ and S.T. Klein∗


  1. [1] T. Koskela, M. Varsta, J. Heikkonen, & K. Kaski, Time series prediction using recurrent SOM with local linear models, Int. J. of Knowledge-Based Intelligent Engineering Systems, 2 (1), 1998, 60–68.
  2. [2] N.H. Packard, J.P. Crutchfield, J.D. Farmer, & R.S. Shaw, Geometry from a time series, Physical Review Letters, 45 (9), 1980, 712–716. doi:10.1103/PhysRevLett.45.712
  3. [3] F. Takens, Detecting strange attractors in turbulence, in D.A. Rand & L.S. Young (Eds.), Dynamical systems and turbulence, Lecture notes in mathematics 898 (Warwick: Springer Verlag, 1980) 366–381.
  4. [4] J.D. Farmer & J.J. Sidorowich, Predicting chaotic time-series, Physical review letters, 59 (8), 1987, 845. doi:10.1103/PhysRevLett.59.845
  5. [5] S. Singh, Multiple forecasting using local approximation, Pattern recognition, 34 (2), 2001, 443–455. doi:10.1016/S0031-3203(99)00214-9
  6. [6] N.M. Islam, S.Y. Liong, K.K. Phoon, & C.Y. Liaw, Forecasting of river flow data with general regression neural network, Proc. International Symposium on Integrated Water Resources Management, Davis, CA, USA, 2001, 285–290.
  7. [7] T.H. Cormen, C.E. Leiserson, & R.L. Rivest, Introduction to algorithms (Location: MIT Press, 1990).
  8. [8] D.Q. Goldin & P.C. Kanellakis, On similarity queries for time-series data: constraint specification and implementation, Proc. of the First International Conference on Principles and Practice of Constraint Programming, Cassis, France, 1995, 137–153.
  9. [9] D.R. Wilson & T. Martinez, Improved heterogeneous distance functions, Journal of Artificial Intelligence Research, 6, 1997, 1–34.
  10. [10] S.L. Lee, S.J. Chun, D.H. Kim, J.H. Lee, & C.W. Chung, Similarity search for multidimensional data sequences, Proc. 16th IEEE Conf. on Data Engineering, Location 2000, 599–608.
  11. [11] C. Faloutsos, M. Ranganathan, & Y. Manolopoulos, Fast subsequence matching in time-series databases. Proc. of the 1994 ACM SIGMOD International Conference on Management of Data, Location 1994, 419–429.
  12. [12] R. Weber, H.J. Schek, & S. Blott, A quantitative analysis and performance study for similarity-search methods in highdimensional spaces, Proc. of 24th International Conference on Very Large Data Bases, Location 1998, 194–205.
  13. [13] M.L. Hetland, A survey of recent methods for efficient retrieval of similar time sequences, in M. Last, A. Kandel, and H. Bunke (Eds.), Data mining in time series databases (Singapore: World Scientific, 2004) page #s.
  14. [14] C. Faloutsos, H.V. Jagadish, A.O. Mendelzon, & T.A. Milo, Signature technique for similarity-based queries, Proc. Compression and Complexity of Sequences ’97 (SEQUENCES ’97), Italy, 1997, page #s.
  15. [15] K.K. Phoon, M.N. Islam, C.Y. Liaw, & S.Y. Liong, A practical inverse approach for forecasting nonlinear hydrological time series, Journal of Hydrologic Engineering, ASCE, 7 (2), 2002, 116–128. doi:10.1061/(ASCE)1084-0699(2002)7:2(116)
  16. [16] V. Babovic & M. Keijzer, Forecasting of river discharges in the presence of chaos and noise, in J. Marsalek (Ed.), Coping with floods: lessons learned from recent experiences, (Location: Kluwer, 1999) page #s.
  17. [17] A.E. Mitchell, A comparison of short-term dispersion estimates resulting from various atmospheric stability classification methods, Atmospheric Environment, 16 (4), 1982, 765–773. doi:10.1016/0004-6981(82)90394-8

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