Nonlinear Time Series Analyses in Industrial Environments and Limitations for Highly Sparse Data

  • Emili Balaguer-Ballester
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 18)

Abstract

This work presents case studies of effective knowledge transfer in projects that focused on using nonlinear time series analyses in varied industrial settings. Applications, characterized by intricate dynamical processes, ranged from e-commerce to predicting services request in support centres. A common property of these time series is that they were originated by nonlinear and potentially high-dimensional systems in weakly stationary environments. Therefore, large amount of data was typically required for providing useful forecasts and thus a successful transfer of knowledge. However, in certain scenarios, classifications or predictions have to be inferred from time windows containing only few relevant patterns. To address this challenge, we suggest here the combined use of statistical learning and time series reconstruction algorithms in industrial domains where datasets are severely limited. These ideas could entail a successful transfer of knowledge in projects were more traditional data mining approaches may fail.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abe, N., Verma, N., Schroko, R., Apte, C.: Cross channel optimized marketing by reinforcement learning. In: Proceedings of the KDD, pp. 767–772 (2004)Google Scholar
  2. Balaguer-Ballester, E., Camps-Valls, G., Carrasco-Rodriguez, J.L., Soria, E., del Valle-Tascon, S.: Effective one-day ahead prediction of hourly surface ozone concentrations in eastern Spain using linear models and neural networks. Ecological Modeling 156, 27–41 (2002)CrossRefGoogle Scholar
  3. Balaguer-Ballester, E., Lapish, C., Seamans, J., Durstewitz, D.: Attracting dynamics of frontal cortex ensembles during memory-guided decision making. PLoS Computational Biology 7(5), e1002057 (2011), doi:10.1371/journal.pcbi.1002057Google Scholar
  4. Balaguer-Ballester, E., Soria, E., Palomares, A., Martín-Guerrero, J.D.: Predicting service request in support centres based on nonlinear dynamics, ARMA modelling and neural Networks. Expert Systems with Applications 34, 665–672 (2008)CrossRefGoogle Scholar
  5. Carberry, S.: Techniques for Plan Recognition. User Modeling and User Adapted Interaction 11, 31–48 (2001)MATHCrossRefGoogle Scholar
  6. Carpenter, G.A., Grossberg, S.: ART2: Self-Organization of Stable Category Recognition Codes for Analog Input Patterns. In: Pattern Recognition by Self-Organizing Neural Networks. MIT Press (1991)Google Scholar
  7. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. John Wiley and Sons (2001)Google Scholar
  8. Fu, Y., Shandu, K., Shih, M.: Fast clustering of web users based on navigation pattern. In: Proceedings of SCI 1999/ISAS1999, Orlando, USA (1999)Google Scholar
  9. Gómez-Pérez, G., Martín-Guerrero, J.D., Soria-Olivas, E., Balaguer-Ballester, E., Palomares, A., Casariego, N.: Assigning discounts in a marketing campaign by using reinforcement learning. Expert Systems with Applications 36, 8022–8831 (2009)CrossRefGoogle Scholar
  10. Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and Evaluating choices in a virtual community of use. In: CHI 1995: Conference Proceedings on Human Factors in Computing Systems, Denver, USA, pp. 194–201 (1995)Google Scholar
  11. Kohonen, T.: Self-Organizing Maps, 2nd edn. Springer, Berlin (1997)MATHCrossRefGoogle Scholar
  12. Martín-Guerrero, J.D., Balaguer-Ballester, E., Camps-Valls, G., Palomares, A., Serrano-López, A.J., Gómez-Sanchís, J., Soria, E.: Machine Learning Methods for One-Session Ahead Prediction of Accesses to Page Categories. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 420–424. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. Martín-Guerrero, J.D., Lisboa, P.J.G., Palomares-Chust, A., Soria, E., Balaguer-Ballester, E.: An approach based on Adaptive Resonance Theory for analyzing the viability of recommender systems in a citizen web portal. Expert Systems with Applications 33, 743–753 (2007)CrossRefGoogle Scholar
  14. Martín-Guerrero, J.D., Soria, E., Gómez-Sanchis, J., Soriano-Asensi, A., Palomares, A., Balaguer-Ballester, E.: Studying the feasibility of a recommender in a citizen Web Portal based on user modeling and clustering algorithm. Expert Systems with Applications 30, 299–312 (2006)CrossRefGoogle Scholar
  15. Pfeifer, P.E., Carraway, R.L.: Modeling customer relationships as markov chains. Journal of Interactive Marketing 14, 43–55 (2000)CrossRefGoogle Scholar
  16. Reichheld, F.F.: The loyalty effect: The hidden force behind growth, profits, and lasting value. Harvard Business School Press, Boston (2001)Google Scholar
  17. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of the Conference on Computer Supported Cooperative Work, pp. 175–186. Chapel Hill (1994)Google Scholar
  18. Sauer, T., Yorke, J., Casdagli, M.: Embedology. J. Stat. Phys. 65, 579–616 (1992)MathSciNetCrossRefGoogle Scholar
  19. Schafer, J.B., Konstan, J., Riedl, J.: Recommender Systems in E-Commerce. In: Proceedings of the First ACM Conference on Electronic Commerce EC 1999, Denver, USA, pp. 158–166 (1999)Google Scholar
  20. Smith, A.J.: Applications of the self-organising map to reinforcement learning. Neural Networks 15, 1107–1124 (2002)CrossRefGoogle Scholar
  21. Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. MIT Press, Cambridge (1998)Google Scholar
  22. Takens, F.: Detecting strange attractors in turbulence. Springer lecture notes in mathematics, vol. 898, pp. 366–381 (1981)Google Scholar
  23. Vapnik, V.N.: The nature of statistical learning. Springer, New York (1999)Google Scholar
  24. Zukerman, I., Albrecht, D.W.: Predictive Statistical Models for User Modeling. User Modeling and User Adapted Interaction 11, 5–18 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Emili Balaguer-Ballester
    • 1
  1. 1.School of Design, Engineering and ComputingBournemouth UniversityDorsetUK

Personalised recommendations