[1]
|
Vamsidhar, E., Varma, K.V.S.R.P., Rao, P. and Satapati, R. (2010) Prediction of Rainfall Using Backpropagation Neural Network Model. International Journal on Computer Science and Engineering, 2, 1119-1121.
|
[2]
|
Wu, C.L. and Chau, K.W. (2013) Prediction of Rainfall Time Series Using Modular Soft Computing Methods. Engineering Applications of Artificial Intelligence, 26, 997-1007. http://dx.doi.org/10.1016/j.engappai.2012.05.023
|
[3]
|
Rodríguez Rivero, C., Herrera, M., Pucheta, J., Baumgartner, J., Patiño, D. and Sauchelli, V. and Laboret, S. (2013) Time Series Forecasting Using Bayesian Method: Application to Cumulative Rainfall. IEEE Latin America Transactions, 11, 359 364. http://dx.doi.org/10.1109/TLA.2013.6502830
|
[4]
|
Gonzalez-Romera, E., Jaramillo-Moran, M.A. and Carmona-Fernandez, D. (2006) Monthly Electric Energy Demand Forecasting Based on Trend Extraction. IEEE Transactions on Power Systems, 21, 1946-4953.
http://dx.doi.org/10.1109/TPWRS.2006.883666
|
[5]
|
Pucheta, J., Patiño, H., Schugurensky, C., Fullana, R. and Kuchen, B. (2007) Optimal Control Based-Neurocontroller to Guide the Crop Growth under Perturbations. Dynamics of Continuous, Discrete And Impulsive Systems Special Volume Advances in Neural Networks-Theory and Applications. DCDIS A Supplement, Advances in Neural Networks, 14, 618-623.
|
[6]
|
Zhang, G., Patuwo, B.E. and Hu, M.Y. (1998) Forecasting with Artificial Neural Networks: The State of Art. Journal of International Forecasting, 14, 35-62. http://dx.doi.org/10.1016/S0169-2070(97)00044-7
|
[7]
|
Pucheta, J., Rodríguez Rivero, C.M., Herrera, M., Salas, C., Patiño, D. and Kuchen, B. (2011) A Feed-Forward Neural Networks-Based Nonlinear Autoregressive Model for Forecasting Time Series. Revista Computación y Sistemas, Centro de Investigación en Computación-IPN, 14, 423-435.
|
[8]
|
Khosravi, A., Nahavandi, S., Creighton, D. and Atiya, A.F. (2011) Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances. IEEE Transactions on Neural Networks, 22, 1341-1356.
|
[9]
|
Bishop, C. (2006) Pattern Recognition and Machine Learning. Springer, Boston.
|
[10]
|
Bishop, C. (1995) Neural Networks for Pattern Recognition. University Press, Oxford.
|
[11]
|
Tresp, V. and Hofmann, R. (1998) Nonlinear Time-Series Prediction with Missing and Noisy Data. Neural Computation, 10, 731-747. http://dx.doi.org/10.1162/089976698300017728
|
[12]
|
Markovsky, I., Willems, J.C. and De Moor, D. (2005) State Representation from Finite Time Series. Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005, Seville, 12-15 December 2005, 832-835.
|
[13]
|
Mandelbrot, B.B. (1983) The Fractal Geometry of Nature. W. H. Freeman, San Francisco.
|
[14]
|
Rodriguez Rivero, C., Pucheta, J., Patiño, H., Baumgartner, J., Laboret, S. and Sauchelli, V. (2013) Analysis of a Gaussian Process and Feed-Forward Neural Networks Based Filter for Forecasting Short Rainfall Time Series. 2013 International Joint Conference on Neural Networks, Dallas, 4-9 August 2013, 1-6.
http://dx.doi.org/10.1109/IJCNN.2013.6706741
|
[15]
|
Glass, L. and Mackey, M.C. (1998) From Clocks to Chaos, the Rhythms of Life. Princeton University Press, Princeton.
|
[16]
|
Pucheta, J., Patiño, H.D. and Kuchen, B. (2007) Neural Networks-Based Time Series Prediction Using Long and Short Term Dependence in the Learning Process. International Symposium on Forecasting (ISF’07), NN3 Forecasting Competition, New York.
|
[17]
|
Abry, P., Flandrin, P., Taqqu, M.S. and Veitch, D. (2003) Self-Similarity and Long-Range Dependence through the Wavelet Lens. In: Doukhan, P., Oppenheim, G. and Taqqu, M., Eds., Theory and Applications of Long-Range Dependence, Birkhäuser, 527-556.
|
[18]
|
Flandrin, P. (1992) Wavelet Analysis and Synthesis of Fractional Brownian Motion. IEEE Transactions on Information Theory, 38, 910-917. http://dx.doi.org/10.1109/18.119751
|
[19]
|
Dieker, T. (2004) Simulation of Fractional Brownian Motion. The Netherlands MSc Theses, University of Twente, Enschede.
|
[20]
|
Pucheta, J., Patino, D. and Kuchen, B. (2009) A Statistically Dependent Approach for the Monthly Rainfall Forecast from One Point Observations. In: Li, D. and Zhao, C., Eds., Computer and Computing Technologies in Agriculture II, Vol. 2, IFIP Advances in Information and Communication Technology, Vol. 294, Springer, Boston, 787-798.
|
[21]
|
Pucheta, J., Rodríguez Rivero, C., Herrera, M., Salas, C., Sauchelli, V. and Patiño, H.D. (2012) Non-Parametric Methods for Forecasting Time Series from Cumulative Monthly Rainfall. In: Martín, O.E. and Roberts, T.M., Eds., Rainfall: Behavior, Forecasting and Distribution, Nova Science Publishers, Inc., New York.
|
[22]
|
Rodríguez Rivero, C., Herrera, M., Pucheta, J., Baumgartner, J., Patiño, D. and Sauchelli, V. (2012) High Roughness Time Series Forecasting Based on Energy Associated of Series. Journal of Communication and Computer, 9, 576-586.
|