Modeling time series of climatic parameters with probabilistic finite automata

Environmental Modelling and Software

Volumn 20, Issue 6, 2005, Pages 753-760

  Mora Lopez L ^a   Mora, J ^b   Morales Bueno, R ^a   Sidrach De Cardona, M ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

^b UNIVERSITY OF ALICANTE   (Spain)

Author keywords

Machine Learning; Modeling Climatic Data; Time Series

Indexed keywords

COMPUTER SIMULATION; FINITE AUTOMATA; LEARNING SYSTEMS; PARAMETER ESTIMATION; POLYNOMIALS; PROBABILITY; RANDOM PROCESSES; SAMPLING; TIME SERIES ANALYSIS;

CONTINUOUS VALUES; DISCRETE VALUES; DYNAMIC DISCRETIZATION; TWO-SAMPLE TESTS;

CLIMATE CHANGE;

ALGORITHM; CLIMATE MODELING; TIME SERIES;

EID: 10644223701     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2004.04.007     Document Type: Article

References (22)

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:(3):1988;269-279
2. Aguiar R., Collares-Pereira M. Tag: A time-dependent, autoregressive, gaussian model for generating synthetic hourly radiation. Solar Energy. 49:(3):1992;167-174
3. Anh V., Lunney K., Peiris S. Stochastic models for characterisation and prediction of time series with long-range dependence and fractality. Environmental Modelling & Software. 12:(1):1997;67-73
4. Bendt P., Collares-Pereira M., Rabl A. The frequency distribution of daily insolation values. Solar Energy. 27:1981;1-5
5. Box G.E.P., Jenkins G.M. Time Series Analysis Forecasting and Control. 1976;Prentice-Hall, USA
6. Brinkworth F.J. Autocorrelation and stochastic modelling of insolation series. Solar Energy. 19:1977;343-347
7. Forbus K.D. Qualitative process theory. Artificial Intelligence. 24:1984;85-168
8. Kemmoku Y., Orita S., Nakagawa S., Sakakibara T. Daily insolation forecasting using a multi-stage neural network. Solar Energy. 66:(3):1999;193-199
9. Kleer J., Brown J.S. A qualitative physics based on confluences. Artificial Intelligence. 24:1984;7-83
10. Krog A., Mian S.I., Haussler D. A Hidden Markov Model that Finds Genes in E. coli DNA. Technical Report UCSC-CRL-93-16. 1993;University of California, Santa-Cruz
11. Kuipers B. Commonsense reasoning about causality deriving behavior from structure. Artificial Intelligence. 24:1984;169-203
12. McMillan A.C., MacIver D., Sukloff W.B. Atmospheric environmental information -an overview with Canadian examples. Environmental Modelling & Software. 15:2000;245-248
13. Mohandes M., Rehman S., Halawani T.O. Estimation of global solar radiation using artificial neural networks. Renewable Energy. 14:(1-4):1998;179-184
14. Mora-López L., Sidrach-de-Cardona M. Characterization and simulation of hourly exposure series of global radiation. Solar Energy. 60:(5):1997;257-270
15. Mora-López L., Sidrach-de-Cardona M. Using probabilistic finite automata to simulate hourly series of global radiation. Solar Energy. 73:(3):2003;235-244
16. Mora-López L., Fortes I., Morales-Bueno R., Triguero F. Dynamic discretization of continuous values from time series. Lecture Notes in Artificial Intelligence. 1810:2000;280-291
17. Nadas A. Estimation of probabilities in the language model of the IBM speech recognition system. IEEE Trans. ASSP. 32:(4):1984;859-861
18. 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
19. Rissanen J. A universal data compression system. IEEE Trans. Inform. Theory. 29:(5):1983;656-664
20. Rohatgi V.K. An Introduction to Probability Theory and Mathematical Statistics. 1976;Wiley, USA
21. Ron D., Singer Y., Tishby N. Learning probabilistic automata with variable memory length. Proceedings of the Seventh Annual Workshop on Computational Learning Theory. 1994
22. Ron D., Singer Y., Tishby N. On the learnability and usage of acyclic probabilistic finite Automata. Journal of Computer and System Sciences. 56:1998;133-152