Dynamic discretization of continuous values from time series

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1810, Issue , 2000, Pages 280-291

  López, Llanos Mora ^a   Ruiz, Inmaculada Fortes ^a   Bueno, Rafael Morales ^a   Ruiz, Francisco Triguero ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

MACHINE LEARNING;

CLASSICAL METHODS; CLEARNESS INDICES; CONTINUOUS VALUE; CONVERSION PROCESS; DYNAMIC DISCRETIZATION; DYNAMIC INFORMATION; DYNAMIC PROCESS; QUALITATIVE CHANGES;

TIME SERIES;

EID: 84974687885     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45164-1_30     Document Type: Conference Paper

References (14)

1. AGUIAR, R.J., COLLARES-PEREIRA, M., CONDE J.P. Simple procedure for generating sequences of daily radiation values using a library of Markov Transition Matrix. Solar Energy, 40, 269-279, 1988. 288
2. BOX, G.E.P., JENKINS, G.M. Time Series Analysis forecasting and control.USA: Prentice Hall, 1976. 280
3. DAGUM, P., GALPER, A. Time series prediction using belief network models. Int.Journal Human-Computer Studies, 42, 617-632, 1995. 281
4. FELLER, W. An Introduction to Probability Theory and Its Applications. Vol. 1. Chapman & Hall, London; John Wiley & Sons, New York, 3rd ed., 1968. 290
5. FORBUS, K.D. Qualitative Process Theory, Artif. Intell. 24: 85-168, 1984. 282
6. KLEER, J., BROWN, J.S. A qualitative physics based on confluences, Artif. Intell. 24: 7-83, 1984. 282
7. KUIPERS, B. Commonsense reasoning about causality: deriving behavior from structure, Artif. Intell. 24: 169-203, 1984. 283
8. MORA-LOPEZ, L., SIDRACH-DE-CARDONA, M. Characterization and simulation of hourly exposure series of global radiation. Solar Energy, 60, No.5, 257-270,1997. 288
9. RABINER, L.R. A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the Seventh Annual Workshop on Computational Learning Theory, 1994.
10. RON, D., SINGER, Y., TISHBY, N. Learning Probabilistic Automata with Variable Memory Length. Proceedings oofthe Seventh Annual Workshop on Computational Learning Theory, 1994. 281
11. RON, D., SINGER, Y., TISHBY, N. On the Learnability and Usage of Acyclic Probabilistic Finite Automata. Journal ofComputer and System Sciences, 56, 133-152, 1998. 281
12. SHORACK, G.R., WELLNER, J.A. Empirical Processes with Applications to Statistics. Wiley Series, New York, 1986. 288
13. VENTURA, D., MARTINEZ, T.R. BRACE: A Paradigm for the Discretization of Continuously Valued Data. Proceedings of the Seventh Florida Artificial Intelligence Research Symposium, 117-21, 1994. 283
14. WILSON, D.R., MARTINEZ, T.R. Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research, 6, 1-34, 1997. 286