Skip to main content

On the Need of New Approaches for the Novel Problem of Long-Term Prediction over Multi-dimensional Data

  • Chapter

Part of the Studies in Computational Intelligence book series (SCI,volume 429)

Abstract

Mining evolving behavior over multi-dimensional structures is increasingly critical for planning tasks. On one hand, well-studied techniques to mine temporal structures are hardly applicable to multi-dimensional data. This is a result of the arbitrary-high temporal sparsity of these structures and of their attribute-multiplicity. On the other hand, multi-label classification over denormalized data do not consider temporal dependencies among attributes.

This work reviews the problem of long-term classification over multidimensional structures to solve planning tasks. For this purpose, firstly, it presents an essential formalization and evaluation method for this novel problem. Finally, it extensively overviews potential relevant contributions from different research streams.

Keywords

  • Training Dataset
  • Sequence Learning
  • Multivariate Adaptive Regression Spline
  • Research Stream
  • Evolve Context

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-30454-5_9
  • Chapter length: 18 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-30454-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   149.99
Price excludes VAT (USA)
Hardcover Book
USD   199.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Antunes, C.: Pattern Mining over Nominal Event Sequences using Constraint Relaxations. Ph.D. thesis, Instituto Superior Tecnico (2005)

    Google Scholar 

  2. Antunes, C.: Temporal pattern mining using a time ontology. In: EPIA, pp. 23–34. Associação Portuguesa para a Inteligéncia Artificial (2007)

    Google Scholar 

  3. Antunes, C.: An ontology-based framework for mining patterns in the presence of background knowledge. In: ICAI, pp. 163–168. PTP, Beijing, China (2008)

    Google Scholar 

  4. Bacchus, F., Kabanza, F.: Using temporal logics to express search control knowledge for planning. A.I. 116, 123–191 (2000)

    MathSciNet  MATH  Google Scholar 

  5. Begleiter, R., El-Yaniv, R., Yona, G.: On prediction using variable order markov models. J. Artif. Int. Res. 22, 385–421 (2004)

    MathSciNet  MATH  Google Scholar 

  6. Bellazzi, R., Ferrazzi, F., Sacchi, L.: Predictive data mining in clinical medicine: a focus on selected methods and applications. Wiley Interdisc, DM and Knowledge Discovery 1(5), 416–430 (2011)

    CrossRef  Google Scholar 

  7. Bellazzi, R., Zupan, B.: Predictive data mining in clinical medicine: Current issues and guidelines. IJ Medical Information 77(2), 81–97 (2008)

    CrossRef  Google Scholar 

  8. Ben Taieb, S., Sorjamaa, A., Bontempi, G.: Multiple-output modeling for multi-step-ahead time series forecasting. Neurocomput. 73, 1950–1957 (2010)

    CrossRef  Google Scholar 

  9. Bengio, S., Fessant, F., Collobert, D.: Use of modular architectures for time series prediction. Neural Proc. Lett. 3, 101–106 (1996)

    CrossRef  Google Scholar 

  10. Bontempi, G., Birattari, M., Bersini, H.: Lazy learning for iterated time-series prediction. In: I.W. on A. Black-Box T. for Nonlinear Modeling, pp. 62–68. Katholieke U.L., Leuven (1998)

    Google Scholar 

  11. Bontempi, G., Ben Taieb, S.: Conditionally dependent strategies for multiple-step-ahead prediction in local learning. Int. J. of Forecasting 27(2004), 689–699 (2011)

    CrossRef  Google Scholar 

  12. Brahim-Belhouari, S., Bermak, A.: Gaussian process for nonstationary time series prediction. Computational Statistics and Data Analysis 47(4), 705–712 (2004)

    MathSciNet  MATH  CrossRef  Google Scholar 

  13. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman & Hall, New York (1984)

    MATH  Google Scholar 

  14. Brown, P.J., Vannucci, M., Fearn, T.: Multivariate bayesian variable selection and prediction. Journal of the Royal Statistical Society 60(3), 627–641 (1998)

    MathSciNet  MATH  CrossRef  Google Scholar 

  15. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167 (1998)

    CrossRef  Google Scholar 

  16. Carrasco, R.C., Oncina, J.: Learning Stochastic Regular Grammars by Means of a State Merging Method. In: Carrasco, R.C., Oncina, J. (eds.) ICGI 1994. LNCS, vol. 862, pp. 139–152. Springer, Heidelberg (1994)

    CrossRef  Google Scholar 

  17. Cheng, H., Tan, P.-N., Gao, J., Scripps, J.: Multistep-Ahead Time Series Prediction. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 765–774. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  18. Cortez, P., Rocha, M., Neves, J.: A Meta-Genetic Algorithm for Time Series Forecasting. In: Proc. of AIFTSA 2001, EPIA 2001, Porto, Portugal, pp. 21–31 (2001)

    Google Scholar 

  19. Cotofrei, P., Stoffel, K.: First-Order Logic Based Formalism for Temporal Data Mining. In: Foundations of Data Mining and knowledge Discovery. SCI, vol. 6, pp. 185–210. Springer, Heidelberg (2005)

    Google Scholar 

  20. Dietterich, T.G., Michalski, R.S.: Discovering patterns in sequences of events. Artif. Intell. 25, 187–232 (1985)

    CrossRef  Google Scholar 

  21. Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.J.: Querying and mining of time series data: experimental comparison of representations and distance measures. In: Proceedings of the VLDB Endowment, vol. 1(2), pp. 1542–1552 (2008)

    Google Scholar 

  22. Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: 5th ACM SIGKDD, KDD, pp. 43–52. ACM, NY (1999)

    Google Scholar 

  23. Fang, Y., Koreisha, S.G.: Updating arma predictions for temporal aggregates. Journal of Forecasting 23(4), 275–296 (2004)

    CrossRef  Google Scholar 

  24. Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proc. of the 2nd European Conf. on Comput. Learning Theory, pp. 23–37. Springer, London (1995)

    Google Scholar 

  25. Guimarães, G.: The Induction of Temporal Grammatical Rules from Multivariate Time Series. In: Oliveira, A.L. (ed.) ICGI 2000. LNCS (LNAI), vol. 1891, pp. 127–140. Springer, Heidelberg (2000)

    CrossRef  Google Scholar 

  26. Henriques, R., Antunes, C.: An integrated approach for healthcare planning over dimensional data using long-term prediction. In: 1st Proc. in Healthcare Inf. Systems. Springer, Beijing (2012)

    Google Scholar 

  27. Hsu, C.N., Chung, H.H., Huang, H.S.: Mining skewed and sparse transaction data for personalized shopping recommendation. Machine Learning 57, 35–59 (2004)

    CrossRef  Google Scholar 

  28. The IEEE Computational Intelligence Society: A k-Nearest Neighbor Based Algorithm for Multi-label Classification 2 (2005)

    Google Scholar 

  29. Ji, Y., Hao, J., Reyhani, N., Lendasse, A.: Direct and Recursive Prediction of Time Series Using Mutual Information Selection. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 1010–1017. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  30. Kersting, K., De Raedt, L., Gutmann, B., Karwath, A., Landwehr, N.: Relational Sequence Learning. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 28–55. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  31. Kleinfeld, D., Sompolinsky, H.: Associative neural network model for the generation of temporal patterns: Theory and application to central pattern generators. Biophysical J. 54(6), 1039–1051 (1988)

    CrossRef  Google Scholar 

  32. Koch, I., Naito, K.: Prediction of multivariate responses with a selected number of principal components. Comput. Statistical Data Analysis 54, 1791–1807 (2010)

    MathSciNet  CrossRef  Google Scholar 

  33. Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York (1994)

    MATH  Google Scholar 

  34. Laxman, S., Sastry, P.S.: A survey of temporal data mining. Sadhana-academy. Proc. in Eng. Sciences 31, 173–198 (2006)

    MathSciNet  MATH  Google Scholar 

  35. Laxman, S., Sastry, P.S., Unnikrishnan, K.P.: Discovering frequent episodes and learning hidden markov models: A formal connection. IEEE Trans. on Knowl. and Data Eng. 17, 1505–1517 (2005)

    CrossRef  Google Scholar 

  36. Lee, T.S., Chiu, C.C., Chou, Y.C., Lu, C.J.: Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis 50(4), 1113–1130 (2006)

    MathSciNet  CrossRef  Google Scholar 

  37. Lesh, N., Zaki, M.J., Ogihara, M.: Mining features for sequence classification. In: Proc. of the 5th ACM SIGKDD, pp. 342–346. ACM, NY (1999)

    Google Scholar 

  38. Liu, J., Yuan, L., Ye, J.: An efficient algorithm for a class of fused lasso problems. In: Proc. of the 16th ACM SIGKDD, KDD, pp. 323–332. ACM, NY (2010)

    Google Scholar 

  39. Lockett, A.J., Miikkulainen, R.: Temporal convolution machines for sequence learning. Tech. Rep. AI-09-04, University of Texas at Austin (2009)

    Google Scholar 

  40. Mannila, H., Toivonen, H., Inkeri Verkamo, A.: Discovery of frequent episodes in event sequences. IJ of DMKD 1, 259–289 (1997)

    Google Scholar 

  41. Meeden, L.A.: An incremental approach to developing intelligent neural network controllers for robots. IEEE Trans. on Sys. Man and Cyber. 26(3), 474–485 (1996)

    CrossRef  Google Scholar 

  42. Moerchen, F.: Tutorial cidm-t temporal pattern mining in symbolic time point and time interval data. In: CIDM. IEEE, Nashville (2009)

    Google Scholar 

  43. Mörchen, F.: Time series knowledge mining. W. in Dissertationen. G&W (2006)

    Google Scholar 

  44. Quinlan, J.R.: Learning with continuous Classes. In: 5th Australian Joint Conf. on Artificial Intelligence, pp. 343–348 (1992)

    Google Scholar 

  45. Sfetsos, A., Siriopoulos, C.: Time series forecasting with a hybrid clustering scheme and pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part A 34(3), 399–405 (2004)

    CrossRef  Google Scholar 

  46. Sorjamaa, A., Hao, J., Reyhani, N., Ji, Y., Lendasse, A.: Methodology for long-term prediction of time series. Neurocomput. 70, 2861–2869 (2007)

    CrossRef  Google Scholar 

  47. Sorjamaa, A., Lendasse, A.: Time series prediction using dirrec strategy. In: ESANN, pp. 143–148 (2006)

    Google Scholar 

  48. Sun, R., Giles, C.L.: Sequence learning: From recognition and prediction to sequential decision making. IEEE Intelligent Systems 16, 67–70 (2001)

    CrossRef  Google Scholar 

  49. Sun, R., Peterson, T.: Autonomous learning of sequential tasks: experiments and analyses. IEEE Transactions on Neural Networks 9(6), 1217–1234 (1998)

    CrossRef  Google Scholar 

  50. Sutton, R.S.: Learning to predict by the methods of temporal differences. Machine Learning 3, 9–44 (1988)

    Google Scholar 

  51. Sutton, R., Barto, A.: Reinforcement learning: an introduction. Adaptive Computation and Machine Learning. MIT Press (1998)

    Google Scholar 

  52. Taieb, S.B., Bontempi, G., Sorjamaa, A., Lendasse, A.: Long-term prediction of time series by combining direct and mimo strategies. In: Proc. of the 2009 IJCNN, pp. 1559–1566. IEEE Press, USA (2009)

    Google Scholar 

  53. Tsoumakas, G., Katakis, I.: Multi Label Classification: An Overview. IJ of Data W. and Mining 3(3), 1–13 (2007)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rui Henriques .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Henriques, R., Antunes, C. (2012). On the Need of New Approaches for the Novel Problem of Long-Term Prediction over Multi-dimensional Data. In: Lee, R. (eds) Computer and Information Science 2012. Studies in Computational Intelligence, vol 429. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30454-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30454-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30453-8

  • Online ISBN: 978-3-642-30454-5

  • eBook Packages: EngineeringEngineering (R0)