Evolutionary and Neural Computation for Time Series Prediction Minisite

               
   
 


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Bibliography

[Kou95] C. Kou and A. Reitsch. Neural networks vs. conventional methods of forecasting. The journal of Buisness Forecasting, pages 17-22, Winter 1995.

[Koz98] N. K. Kasabov R. Kozma and J. S. Kim. Integration of connectionist methods and chaotic time-series analysis for the prediction of process data. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, (13):519{538, 1998.

[Hit99] Hitoshi Iba. Using genetic programming to predict Financial data. In Congress on Evolutionary Computation, 1999.

[Nas99] Guy P. Nason and Rainer von Sachs. Wavelets in time series analysis. Philosophical Transactions of the Royal Society of London A, 357(1760):2511-2526, 1999.

[May99] Helmut A. Mayer and Roland Schwaiger. Evolutionary and coevolutionary approaches to time series prediction using generalized multi-layer perceptrons. In Proceedings of the Congress on Evolutionary Computation, volume 1, 6-9 1999.

[Zha00] Byoung-Tak Zhang and Dong-Yeon Cho. Evolving neural trees for time series prediction using bayesian evolutionary algorithms". In The First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, 2000.

[Val03] Barton A. Valdes, J. Mining multivariate time series models with soft-computing techniques: A coarse-grained parallel computing approach. In Lecture Notes in Computer Science, 2003.

[Rum86] D. Rumelhart, G. Hinton, and R.J. Williams. Parallel Distributed Processing, chapter Learning internal representations by error propagation. MIT Press., Cambridge, 1986.

[Zal00] J.M. Zaldivar,I.M. Galvan F. Strozzi E. Gutierrez and A. Tomasin. Forecasting high waters at venice lagoon using chaotic time series analysis and nonlinear neural networks. Journal of Hydroinformatics, (02.1):61-84, 2000.

[Sta97] P. Stagge and B. Senho . An extended elman net for modelling time series. In International Conference on Arti cial Neural Networks, 1997.

[Cho97] Tomasz J. Cholewo and Jacek M. Zurada. Neural network tools for stellar light prediction. In IEEE Aerospace Conference,1997.

[Tsa02] V.M. Mladenov A. C. Tsakoumis, S. S. Vladov. Electric load forecasting with multilayer percetron and elman neural network. In IEEE NEUREL, 2002.

[Gil01] S. LAWRENCE C. L. GILES and AH C. TSOI. Noisy time series prediction using recurrent neural networks and grammatical inference. Machine Learning,, (43):161183, 2001.

[Sit02] C. SITTE and J. SITTE. Neural networks approach to the randomwalk dilemmaof nancial time series. Applied Intelligence,(16):163171, 2002.

[Jor86a] M.I. Jordan. Attractor dynamics and parallelism in a connectionist sequential machine. In Proc. of the Eighth Annual Conference of the Cognitive Science Society, pages 531-546. NJ: Erlbaum, 1986.

[Jor86b] M.I. Jordan. Serial order: A parallel distributed processing approach. Technical report, Institute for Cognitive Science. University of California, 1986.

[Elm90] J.L. Elman. Finding structure in time. Cognitive Science, 14:179 211, 1990.

[Gal01] I.M. Galvan and P. Isasi. Multi-step learning rule for recurrent neural models: An application to time series forecasting. Neural Processing Letters, (13):115{133, 2001.

[Mey90] T.P. Meyer, N. H. Packard: Local Forecasting of High-Dimensional Chaotic Dynamics, Nonlinear modeling and forecasting. 1990; editors, Martin Casdagli, Stephen Eubank, pp. 249-263.

[Luq04] Luque, C., Isasi P., Hernandez, J.C., Forecasting Time Series by means of Evolutionary Algorithms. Proceedings of Parallel Problem Solving from Nature - PPSN VIII, vol 3242 (2004), pp. 1058--1067

[Boo89] L.B. Booker, D.E. Goldberg, J.H. Holland: Classifier Systems and Genetic Algorithms. Artificial Intelligence No 40 (1989), pp. 235-282.

[Smi80]S.F. Smith: A Learning System Based on Genetic Adaptative Algorithms. Ph.D. Thesis, University of Pittsburgh (1980).

[Jan93] C.Z Janikow: A Knowledge Intensive Genetic Algorithm for Supervised Learning. Machine Learning 13 (1993), pp. 189-228.

[Dej93] K.A. De Jong, W.M. Spears, F.D. Gordon: Usign Genetic Algorithms for Concept Learning. Machine Learning 13 (1993), pp. 198-228.

[Mac77] M.C. Mackey and L. Glass: Oscillation and chaos in physiological control systems. Science, Vol 197: 287-289, 1977

[Ying97] L. Yingwei and N. Sundararajan and P. Saratchandran: A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks. Neural Computation. Vol 9: 461-478, 1997

[Hur95] Hurrell, J.W., 1995: Decadal trends in the North Atlantic Oscillation and relationships to regional temperature and precipitation. Science 269, 676-679


2005, University CARLOS III of Madrid