Associative Model for the Forecasting of Time Series Based on the Gamma Classifier

  • Itzamá López-Yáñez
  • Leonid Sheremetov
  • Cornelio Yáñez-Márquez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)


The paper describes a novel associative model for the forecasting of time series in petroleum engineering. The model is based on the Gamma classifier, which is inspired on the Alpha-Beta associative memories, taking the alpha and beta operators as basis for the gamma operator. The objective is to reproduce and predict future oil production in different scenarios in an adjustable time window. The distinctive features of the experimental data set are spikes, abrupt changes and frequent discontinuities, which considerably decrease the precision of traditional forecasting methods. As experimental results show, this classifier-based predictor exhibits competitive performance. The advantages and limitations of the model, as well as lines of improvement, are discussed.


Time series forcasting associative models oil production time series Gamma classifier 


  1. 1.
    Schelter, B., Winterhalder, M., Timmer, J. (eds.): Handbook of Time Series Analysis. Wiley, Weinheim (2006)zbMATHGoogle Scholar
  2. 2.
    van Golf-Racht, T.D.: Fundamentals of Fractured Reservoir Engineering, Developments in Petroleum Science, vol. 12. Elsevier, Amsterdam (1982)Google Scholar
  3. 3.
    Palit, A.K., Popovic, D.: Computational Intelligence in Time Series Forecasting. Springer, London (2005)zbMATHGoogle Scholar
  4. 4.
    Sheremetov, L., Alvarado, M., Bañares-Alcántara, R., Anminzadeh, F.: Intelligent Computing in Petroleum Engineering (Editorial). J. of Petroleum Science and Engineering 47(1-2), 1–3 (2005)CrossRefGoogle Scholar
  5. 5.
    He, Z., Yang, L., Yen, J., Wu, C.: Neural-Network Approach to Predict Well Performance Using Available Field Data. In: SPE Western Regional Meeting, Bakersfield, California, March 26-30, SPE 68801 (2001)Google Scholar
  6. 6.
    Kim, D., Kim, C.: Forecasting Time Series with Genetic Fuzzy Predictor Ensemble. IEEE Tr. on Fuzzy Systems 5(4), 523–535 (1997)CrossRefGoogle Scholar
  7. 7.
    Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis (Prentice Hall series in statistics), 5th edn. Prentice Hall (2001)Google Scholar
  8. 8.
    López-Yáñez, I., Argüelles-Cruz, A.J., Camacho-Nieto, O., Yáñez-Márquez, C.: Pollutants Time-Series Prediction Using the Gamma classifier. Int. J. of Computational Intelligence Systems 4(4), 680–711 (2011)Google Scholar
  9. 9.
    Acevedo-Mosqueda, M.E., Yáñez-Márquez, C., López-Yáñez, I.: Alpha-Beta Bidirectional Associative Memories: Theory and Applications. Neural Processing Letters 26(1), 1–40 (2007)CrossRefGoogle Scholar
  10. 10.
    Yáñez, C., Felipe-Riveron, E., López-Yáñez, I., Flores-Carapia, R.: A Novel Approach to Automatic Color Matching. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 529–538. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Aizenberg, I., Moraga, C.: Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm. Soft Computing 11, 169–183 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Itzamá López-Yáñez
    • 1
    • 2
  • Leonid Sheremetov
    • 1
  • Cornelio Yáñez-Márquez
    • 3
  1. 1.Mexican Petroleum Institute (IMP)Mexico CityMexico
  2. 2.Instituto Politécnico NacionalCentro de Investigación y Desarrollo Tecnológico en Cómputo (CIDETEC - IPN)Mexico CityMexico
  3. 3.Instituto Politécnico NacionalCentro de Investigación en Computación (CIC - IPN)Mexico CityMexico

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