Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

4 Issues per year

Open Access
See all formats and pricing
More options …

Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets

Cristian Rodrìguez Rivero
  • Corresponding author
  • Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba, Argentina
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Juliàn Pucheta
  • Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba, Argentina
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Sergio Laboret
  • Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba, Argentina
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Vìctor Sauchelli
  • Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba, Argentina
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Daniel Patiǹo
  • Advanced Intelligent Systems Laboratory, Institute of Automatic Universidad Nacional de San JuanSan Juan, Argentina
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-12-17 | DOI: https://doi.org/10.1515/jaiscr-2017-0001


This article presents short-term predictions using neural networks tuned by energy associated to series based-predictor filter for complete and incomplete datasets. A benchmark of high roughness time series from Mackay Glass (MG), Logistic (LOG), Henon (HEN) and some univariate series chosen from NN3 Forecasting Competition are used. An average smoothing technique is assumed to complete the data missing in the dataset. The Hurst parameter estimated through wavelets is used to estimate the roughness of the real and forecasted series. The validation and horizon of the time series is presented by the 15 values ahead. The performance of the proposed filter shows that even a short dataset is incomplete, besides a linear smoothing technique employed; the prediction is almost fair by means of SMAPE index. Although the major result shows that the predictor system based on energy associated to series has an optimal performance from several chaotic time series, in particular, this method among other provides a good estimation when the short-term series are taken from one point observations.

Keywords: short time series; forecasting; missing data; energy associated to series; complete and incomplete datasets


  • [1] Little, R.J.A. and D.B. Rubin, Statistical Analysis with Missing Data. John Wiley Publishers Company, 2002.Google Scholar

  • [2] Tsikriktsis, N., A review of techniques for treating missing data in OM survey research, Journal of Operations Management 24, 2005, pp. 53-62.CrossrefGoogle Scholar

  • [3] Maravall A, Pea D., Missing observations and additive outliers in time series models. In: Mariano RS (ed) Advances in statistical analysis and statistical computing. JAI Press, Stanford, 1986.Google Scholar

  • [4] Mitat Uysal, Reconstruction of Time Series Data with Missing Values, Journal of Applied Sciences, 7 (6): ISSN 1812-5654, 2007, pp. 922-925 .Google Scholar

  • [5] Kornelsen, K., & Coulibaly, P., Comparison of Interpolation, Statistical, and Data-Driven Methods for Imputation of Missing Values in a Distributed Soil Moisture Dataset. Journal of Hydrologic Engineering, 19(1), 2012, pp. 26-43.CrossrefGoogle Scholar

  • [6] Kharin, Yu.S. and Huryn, A.S., Statistical analysis and forecasting of autoregressive time series under missing values. Bulletin of the International Statistic Institute, 1, 2003, pp. 612-613.Google Scholar

  • [7] Hong, B. and CH. Chen, Radial basis function neural network-based nonparametric estimation approach for missing data reconstruction of nonstationary series. IEEE Int. Conf. Neural Networks and Signal Processing Nanjing, China, December, 14-17, 2003, pp. 75-78.Google Scholar

  • [8] Coulibaly P. Comparison of neural network methods for infilling missing daily weather records. Journal of Hydrology. v. 341, 2007, pp. 27-41.Google Scholar

  • [9] Kidson, J.W., and K.E. Trenberth, Effects of missing data on estimates of monthly mean general circulation statistics, J. Climate, 1, 1988, pp. 1261-1275.CrossrefGoogle Scholar

  • [10] Vincent, L. A., and D. W. Gullet., Canadian historical and homogeneous temperature datasets for climate change analyses. International Journal of Climatology. 19, 1999, 1375-1388.Google Scholar

  • [11] Rodrguez Rivero, Cristian; Patio, Hector Daniel; Pucheta, Julian Antonio, Short-term rainfall time series prediction with incomplete data, in Neural Networks (IJCNN), 2015 International Joint Conference on, 2015, pp. 1-6, 10.1109/IJCNN.2015.7280315.Google Scholar

  • [12] Wasito, I.: 2003, Least Squares Algorithms with Nearest Neighbour Techniques for Imputing Missing Data Values, PhD thesis, University of London.Google Scholar

  • [13] Nelwamondo, F. V., Mohamed, S. and Marwala, T.: n.d., Missing data: A comparison of neural networks and expectation maximization techniques, Current Science 93(11), 2007.Google Scholar

  • [14] Cristian Rodrguez Rivero, Julin Pucheta, Sergio Laboret, Daniel Patio, Vctor Sauchelli. Forecasting short time series with missing data by means of energy associated of series. Applied Mathematics, 6, 2015, pp. 1611-1619, http://dx.doi.org/10.4236/am.2015.69143.CrossrefGoogle Scholar

  • [15] Richman, M. B.; Trafalis, T. B.; Adrianto I.; Missing data imputation through machine learning algorithms, in Artificial Intelligence Methods in the Environmental Sciences. Ed. by H. Sue Ellen, P. Antonello, M. Caren. Springer Netherlands Press, 2009, pp.153-169, doi:CrossrefGoogle Scholar

  • [16] Haviluddina, Ahmad Jawahir, Comparing of ARIMA and RBFNN for short-term forecasting, International Journal of Advances in Intelligent Informatics, Vol. 1, No 1, 2015, pp. 15-22.Google Scholar

  • [17] Zhang G. P., Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 2003, pp. 159-175.CrossrefGoogle Scholar

  • [18] Schneider, T., Analysis of incomplete climate data: estimation of mean values and covariance matrices and imputation of missing values. J. Climate, 14, 2001, pp. 853-871.CrossrefGoogle Scholar

  • [19] C. Rodrguez Rivero, M. Herrera, J. Pucheta, J. Baumgartner, D. Patio and V. Sauchelli. High roughness time series forecasting based on energy associated of series, Journal of Communication and Computer, USA, David Publishing Company, Vol. 9 No. 5, 2012, pp. 576-586, ISSN 1548-7709.Google Scholar

  • [20] C. Rodriguez Rivero, J. Pucheta, H. Patio, J. Baumgartner, S. Laboret and V. Sauchelli. 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. doi:CrossrefGoogle Scholar

  • [21] Kohn, R., & Ansley, C. F., Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data, Journal of the American Statistical Association, 81, 1986, pp. 751-761.CrossrefGoogle Scholar

  • [22] Jones R. H., Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations, Technometrics, 22(3), 1980, pp. 389-395.CrossrefGoogle Scholar

  • [23] Shumway R., and D. Stoffer, An approach to time series smoothing and forecasting using the EM algorithm, Journal of Time Series Analysis, 3, 1982, pp. 253-264.CrossrefGoogle Scholar

  • [24] Tresp V., & Hofmann R., Nonlinear Time-Series Prediction with Missing and Noisy Data. Neural Computation, 10, 1998, pp. 731-747.CrossrefGoogle Scholar

  • [25] Mandelbrot B. B., The Fractal Geometry of Nature, Freeman, San Francisco, CA, 1983.Google Scholar

  • [26] Dieker, T. Simulation of fractional Brownian motion. MSc theses, University of Twente, Amsterdam, The Netherlands, 2004.Google Scholar

  • [27] C. Rodrguez Rivero, J. Pucheta, J. Baumgartner, M. Herrera, D. Patio y B. Kuchen. A NN-based model for time series forecasting in function of energy associated of series, Proc. of the International Conference on Applied, Numerical and Computational Mathematics (ICANCM11), Barcelona, Spain, 2011, pp. 80-86, ISBN 978-1-61804-030-5. Google Scholar

  • [28] Pucheta J., Patio H. D., Kuchen B., A Statistically Dependent Approach for the Monthly Rainfall Forecast from One Point Observations, In Proc. of the Second IFIP Conference on Computer and Computing Technologies in Agriculture (CCTA2008) 2008, Beijing, China.Google Scholar

  • [29] Glass L. and M. C. Mackey. From Clocks to Chaos, The Rhythms of Life. Princeton University Press, Princeton, NJ, 1988.Google Scholar

  • [30] Robert M. May, Simple mathematical models with very complicated dynamics, Nature, vol.261, 1976, pp. 459-467.Google Scholar

  • [31] Henon M., A two-dimensional mapping with a strange attractor. Communications in Mathematical Physics. Vol. 50, 1976, pp. 69-77.Google Scholar

  • [32] Abry, P.; P. Flandrin, M.S. Taqqu, D. Veitch., Self-similarity and long-range dependence through the wavelet lens. Theory and applications of longrange dependence, Birkhuser, 2003, pp. 527-556.Google Scholar

  • [33] Bardet, J.-M.; G. Lang, G. Oppenheim, A. Philippe, S. Stoev, M.S. Taqqu. Semi-parametric estimation of the long-range dependence parameter: a survey. Theory and applications of longrange dependence, Birkhuser, 2003, pp. 557-577.Google Scholar

  • [34] S. F. Crone, M. Hibon, and K. Nikolopoulos, Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction, International Journal of Forecasting, vol. 27, 2011, pp. 635-660.Google Scholar

  • [35] Rubin D. B., Multiple Imputation for Nonresponse in Surveys, New York:Wiley, 1987.Google Scholar

  • [36] Schafer J., Analysis of Incomplete Multivariate Data, Chapman & Hall, 1997.Google Scholar

  • [37] Armstrong J.S. (Ed.) Principles offorecasting: Handbook for researchers and practitioners. Kluwer, 2001.Google Scholar

  • [38] Makridakis, S. & M. Hibon, The M3-Competition: Results, conclusions and implications, International Journal of Forecasting, 16, 2000, pp. 451-476.Google Scholar

  • [39] Shang Zhaowei, Zhang Lingfeng, Ma Shangjun, Fang Bin, Zhang Taiping. Incomplete Time Series Prediction Using Max-Margin Classification of Data with Absent Features Hindawi Publishing Corporation. Mathematical Problems in Engineering, Volume 2010, Article ID 513810, doi:CrossrefGoogle Scholar

  • [40] C. Zecchin, A. Facchinetti, G. Sparacino, G. De Nicolao, C. Cobelli, A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 5653-5656.Google Scholar

  • [41] L.P. Wang and X.J. Fu, Data Mining with Computational Intelligence, Springer, Berlin, 2005.Google Scholar

  • [42] Y. Ren, P. N. Suganthan, N. Srikanth, G. Amaratunga, Random Vector Functional Link Network for Short-term Electricity Load Demand Forecasting, Information Sciences, 2016.Google Scholar

  • [43] L. P. Wang and Shekhar Gupta, Neural networks and wavelet de-noising for stock trading and prediction, Time Series Analysis, Modeling and Applications, Witold Pedrycz and Shyi Ming Chen (eds.), Springer, 2013, pp. 229-247.Google Scholar

  • [44] L. P. Wang, K.K. Teo, and Z.P. Lin, Predicting time series with wavelet packet neural networks, 2001 IEEE International Joint Conference on Neural Networks (IJCNN 2001), 2001, pp. 1593-1597.Google Scholar

  • [45] Dong-Chul Park, ”A Time Series Data Prediction Scheme Using Bilinear Recurrent Neural Network,” 2010 International Conference on Information Science and Applications (ICISA), 2010, pp. 1-7.Google Scholar

  • [46] M. Zhu and L. P. Wang, Intelligent trading using support vector regression and multilayer perceptrons optimized with genetic algorithms, in The 2010 International Joint Conference on Neural Networks (IJCNN), 2010, pp. 1-5.Google Scholar

  • [47] K.K. Teo, L.P. Wang, Z.P. Lin, Wavelet packet multi-layer perceptron for chaotic time series prediction: effects of weight initialization, Computational Science - ICCS 2001, Proceedings Pt 2, Volume: 2074, 2001, pp. 310-317.Google Scholar

  • [48] J. Pucheta, D. Patio and B. Kuchen. Neural Networks-Based Time Series Prediction Using Long and Short Term Dependence in the Learning Process”. In proc. of the 2007 International Symposium on Forecasting, New York 2007.Google Scholar

  • [49] C.-N. Ko, C.-M. Lee, Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended kalman filter, Energy 49, 2013, pp. 413-422.CrossrefGoogle Scholar

  • [50] R.-A. Hooshmand, H. Amooshahi, M. Parastegari, A hybrid intelligent algorithm based short-term load forecasting approach, International Journal of Electrical Power & Energy Systems 45, 2013, pp. 313-324.Google Scholar

  • [51] Kamal S. Selim and Gihan A. Elanany, A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering, Advances in Fuzzy Systems, vol. 2013, Article ID 494239, 2013, doi:CrossrefGoogle Scholar

About the article

Published Online: 2016-12-17

Published in Print: 2017-01-01

Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2017-0001.

Export Citation

© 2016. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

Comments (0)

Please log in or register to comment.
Log in