Modeling ambient air temperature time series using neural networks

Journal of Geophysical Research Atmospheres

Volumn 103, Issue D16, 1998, Pages 19509-19517

  Mihalakakou, G ^a,b   Santamouris, M ^a,b   Asimakopoulos, D ^a,b  

^a UNIVERSITY OF ATHENS   (Greece)

^b NATIONAL OBSERVATORY OF ATHENS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

AIR TEMPERATURE; AMBIENT AIR; ARTIFICIAL NEURAL NETWORK; MODELING; TIME SERIES;

EID: 0032572943     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/98JD02002     Document Type: Article

References (38)

1. Bardossy, A., and L. Duckstein, Fuzzy Rule-Based Modeling With Applications to Geophysical, Biological and Engineering Systems, CRC Press, Boca Raton, Fla., 1995.
2. Box, G. E. P., and G. M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, Merrifield, Va., 1970.
3. Carson, J. E., Analysis of soil and air temperatures by Fourier techniques, J. Geophys. Res., 68, 2217-2232, 1963.
4. Chakraborty, K., K. Mehrotra, C. K. Mohan, and S. Ranka, Forecasting the behavior of multivariate time series using neural networks, J. Neural Networks, 5, 961-970, 1992.
5. Chu, P. S., and R. W. Katz, Measures of predictability with applications to the Southern Oscillation, J. Mon. Weather Rev., 115, 1542-1549, 1987.
6. Cichocki, A., and R. Unbehauen, Neural Networks for Optimization and Signal Processing, John Wiley, New York, 1993.
7. Connor, J. T., D. R. Martin, and L. E. Atlas, Recurrent neural networks and robust time series prediction, IEEE Transaction on Neural Networks, 5, 240-253, 1994.
8. Dash, P. K., G. Ramakrishna, A. C. Liew, and S. Rahman, Fuzzy neural networks for time-series forecasting of electric load, IEE Proc. Gen. Trans. Distrib., 142, 535-544, 1995.
9. Demuth, H., and M. Beale, Neural Network Toolbox User's Guide for MATLAB, The Math Works Inc., Natick, Mass., 1994.
10. Eisenstein, E., I. Kanter, D. A. Kessler, and W. Kinzel, Generation and Prediction of Time Series by a Neural Network, J. Phys. Rev. Lett., 74, 6-9, 1995.
11. Elsner, J. B., Predicting time series using a neural network as a method of distinguishing chaos from noise, J. Phys. A Math Gen., 25, 843-850, 1992.
12. Farmer, J. D., and J. J. Sidorowich, Predicting chaotic time series, J. Phys. Rev. Lett., 59, 845-849, 1987.
13. Gan, T. Y., A time series model for estimating the weekly winter maximum temperature of Northwest Territories, J. Appl. Meteorol., 34, 847-860, 1995.
14. Hipel, K. W., A. I. McLeod, and W. C. Lennox, Advances in Box-Jenkins modeling, 1, Model construction, J. Water Resour. Res., 13, 567-575, 1977.
15. Hondou, T., and Y. Sawada, Analysis of learning processes of chaotic time series by neural networks, J. Prog. Theor. Phys., 91, 397-402, 1994.
16. Kalman, R. E., A new approach of linear filtering and prediction problems, ASME, J. Basic Eng., 82, 35-45, 1960.
17. Kashyap, R. L., and A. R. Rao, Dynamic Stochastic Models From Empirical Data, 334 pp., Academic, San Diego, Calif., 1976.
18. Lamba, B. S., and N. N. Khambete, Analysis of soil temperature at various depths by Fourier technique, J. Mausam, 42, 269-274, 1991.
19. Lapedes, A., and R. Farber, Nonlinear signal processing using neural networks prediction and system modelling, Tech. Rep. LA-UR-87-2662, Los Alamos Natl. Lab., Los Alamos, N. M., 1987.
20. Li, M., K. Mehrotra, C. K. Mohan, and S. Ranka, Sunspot numbers forecasting using neural networks, in Proceedings of the IEEE Symposium on Intelligent Control, vol. 1, pp. 524-529, Inst. of Electr. and Electron. Eng., New York, 1990.
21. Ljung, L., System Identification: Theory for the User, Prentice-Hall, Englewood Cliffs, N. J., 1987.
22. Makhoul, J., Linear prediction: A tutorial review, Proc. IEEE, 63, 561-580, 1975.
23. MATLAB, High Performance Numeric Computation and Visualisation Software, The Math Works Inc., Natick, Mass., 1992.
24. Pham, D. T., and X. Liu, Neural networks for identification, prediction, and control, Springer-Verlag, New York, 1995.
25. Priestley, M. B., Spectral Analysis and Time Series, vols. I and II, 992 pp., Academic, San Diego, Calif., 1982.
26. Rao, A. R., C. H. Hsieh, and G. D. Jeong, Prediction of Nonstationary Climatic Series, J. Theor. Appl. Climatol., 46, 75-87, 1992.
27. Richardson, C. W., Stochastic simulation of daily precipitation, temperature and solar radiation, J. Water Resour. Res., 17, 182-190, 1981.
28. Rosenblatt, F., Principles of Neurodynamics, Spartan Press, Washington, D. C., 1961.
29. Rumelhart, D. E., G. E. Hinton, and R. J. Williams, Learning internal representations by error propagation, in Parallel Distributed Processing, edited by D. E. Rumelhart and J. L. McClelland, pp. 318-362, MIT Press, Cambridge, Mass., 1986.
30. Rumelhart, D. E., R. Durbin, R. Golden, and Y. Chauvin, Backpropagation: The basic theory, in Backpropagation: Theory, Architectures and Applications, Lawrence Erlbaum Assoc., Mahwah, N. J., 1995.
31. Salas, J. D., and T. B. Obeysekera, ARMA model identification of hydrologic time series, J. Water Resour. Res., 18, 1011-1021, 1982.
32. Soderstrom, T., and P. Stoika, System Identification, Prentice-Hall, Englewood Cliffs, N. J., 1989.
33. Stokbro, K., D. K. Umberger, and J. A. Hertz, Exploiting neurons with localized receptive fields to learn chaos, Tech. Rep. Nordita-90/28S, Denmark, 1990.
34. Teodorescu, D., Time series-information and prediction, Biol. Cybern., 63, 477-485, 1990.
35. Thiebaux, H. J., and M. A. Pedder, Spatial Objective Analysis, Academic, San Diego, Calif., 1987.
36. Weibel, A., T. Hanazawa, G. Hinton, K. Shikano, and K. Lang, Phoneme recognition using time-delay neural networks, in Backpropagation: Theory, Architectures and Applications, Lawrence Erlbaum Assoc., Mahwah, N. J., 1995.
37. Weigend, A. S., B. A. Huberman, and D. E. Rumelhart, Predicting the future: A connectionist approach, Int. J. Neural Syst., 1, 193-209, 1990.
38. Wong, F. S., Time series forecasting using backpropagation neural networks, Neurocomputing, 2, 147-159, 1991.