Abstract
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.
Chapter PDF
Similar content being viewed by others
References
Schelter, B., Winterhalder, M., Timmer, J. (eds.): Handbook of Time Series Analysis. Wiley, Weinheim (2006)
van Golf-Racht, T.D.: Fundamentals of Fractured Reservoir Engineering, Developments in Petroleum Science, vol. 12. Elsevier, Amsterdam (1982)
Palit, A.K., Popovic, D.: Computational Intelligence in Time Series Forecasting. Springer, London (2005)
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)
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)
Kim, D., Kim, C.: Forecasting Time Series with Genetic Fuzzy Predictor Ensemble. IEEE Tr. on Fuzzy Systems 5(4), 523–535 (1997)
Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis (Prentice Hall series in statistics), 5th edn. Prentice Hall (2001)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
López-Yáñez, I., Sheremetov, L., Yáñez-Márquez, C. (2013). Associative Model for the Forecasting of Time Series Based on the Gamma Classifier. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Rodríguez, J.S., di Baja, G.S. (eds) Pattern Recognition. MCPR 2013. Lecture Notes in Computer Science, vol 7914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38989-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-38989-4_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38988-7
Online ISBN: 978-3-642-38989-4
eBook Packages: Computer ScienceComputer Science (R0)