Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Prologue Kapitel lesen Erstes Kapitel lesen

2012 | OriginalPaper | Buchkapitel

12. Dynamic Learning of Multiple Time Series in a Nonstationary Environment

verfasst von : Harya Widiputra, Russel Pears, Nikola Kasabov

Erschienen in: Learning in Non-Stationary Environments

Verlag: Springer New York

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This chapter introduces two distinct solutions to the problem of capturing the dynamics of multiple time series and the extraction of useful knowledge over time. As these dynamics would change in a nonstationary environment, the key characteristic of the methods is the ability to evolve their structure continuously over time. In addition, reviews of existing methods of dynamic single time series analysis and modeling such as the dynamic neuro-fuzzy inference system and the neuro-fuzzy inference method for transductive reasoning, which inspired the proposed methods, are presented. This chapter also presents a comprehensive evaluation of the performance of the proposed methods on a real-world problem, which consists of predicting movement of global stock market indexes over time.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Interval Approach for Evolving Granular System Modeling
Nächstes Kapitel Optimizing Feature Calculation in Adaptive Machine Vision Systems
Literatur
1.
Amari, S.: Mathematical foundations of neuro-computing. Proceedings of the IEEE 78 (9), pp. 1443–1463 (1990)CrossRef
2.
Antoniou, A., Pescetto, G., Violaris, A.: Modelling international price relationships and interdependencies between the stock index and stock index futures markets of three EU countries: A multivariate analysis. Journal of Business Finance & Accounting, 30, pp. 645–667 (2003)CrossRef
3.
Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering gene expression patterns. Journal of Computational Biology, 6 (3/4), pp. 281–297 (1999)CrossRef
4.
Bosnic, Z., Kononenko, I., Robnik-Sikonja, M., Kukar, M.: Evaluation of prediction reliability in regression using the transduction principle. In The IEEE region 8 EUROCON 2003, computer as a tool, 2, pp. 99–103 (2003)
5.
Collins, D., Biekpe, N.: Contagion and interdependence in African stock markets. South African Journal of Economics, 71 (1), pp. 181–194 (2003)CrossRef
6.
Friedman, L., Nachman, P.: Using Bayesian networks to analyze expression data. Journal of Computational Biology, 7, pp. 601–620 (2000)CrossRef
7.
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: Data mining, inference, and prediction — second edition. Springer, New York Berlin Heidelberg, Germany (2009)
8.
Holland, J., Holyoak, K., Nisbett, R., Thagard, P.: Induction processes of inference, learning, and discovery. Cambridge University Press, Cambridge, U.S.A. (1989)
9.
Joachims, T.: Transductive inference for text classification using support vector machines. In Proceedings of the sixteenth international conference on machine learning, ICML 1999, pp. 200–209. San Francisco, CA, USA (1999)
10.
Joachims, T.: Transductive learning via spectral graph partitioning. In International conference on machine learning (ICML), pp. 290–297. Washington, DC U.S.A. (2003)
11.
Kasabov, N. (2001).: Evolving fuzzy neural networks for supervised/unsupervised on-line knowledge-based learning. IEEE Transactions on Systems, Man and Cybernetics, 31, pp. 902–918 (2001)
12.
Kasabov, N. (2007).: Global, local and personalised modelling and pattern discovery in Bioinformatics: An integrated approach. Pattern Recognition Letters, 28 (6), pp. 673–685 (2007)
13.
Kasabov, N., Chan, Z., Jain, V., Sidorov, I., Dimitrov, D.: Gene regulatory network discovery from time-series gene expression data: a computational intelligence approach. In Lecture Notes in Computer Science 3316, pp. 1333–1353. Springer Berlin / Heidelberg (2004)
14.
Kasabov, N., Pang, S.: Transductive support vector machines and applications in Bioinformatics for promoter recognition. In Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, 1, pp. 1–6. IEEE Press (2003)
15.
Kasabov, N., Song, Q.: DENFIS: dynamic evolving neural fuzzy inference system and its application for time-series prediction. IEEE Transactions on Fuzzy Systems, 10, pp. 144–154 (2002)CrossRef
16.
Kim, T., Adali, T.: Approximation by fully complex multilayer perceptrons. Neural Computing, 15, pp. 1641–1666 (2003)MATHCrossRef
17.
Kukar, M.: Transductive reliability estimation for medical diagnosis. Artificial Intelligence in Medicine, 29 (1-2), pp. 81–106 (2003)CrossRef
18.
Lei, Z., Yang, Y., Wu, Z.: Ensemble of support vector machine for text-independent speaker recognition. International Journal of Computer Science and Network Security, 6 (5), pp. 163–167 (2006)
19.
Li, C., Yuen, P.: Transductive learning: Learning iris data with two labelled data. In G. Dorffner, H. Bischof, K. Hornik (Eds.), Lecture Notes in Computer Science, Artificial neural networks, ICANN 2001, 2130, pp. 231–236. Springer Berlin / Heidelberg (2001)
20.
Li, F., Wechsler, H.: Watch list face surveillance using transductive inference. In D. Zhang A. Jain (Eds.), Lecture Notes in Computer Science, Biometric authentication, 3072, pp. 1–15. Springer Berlin/Heidelberg (2004)
21.
Li, J., Chua, C.: Transductive inference for color-based particle filter tracking. In Proceedings of international conference on image processing, ICIP 2003, 3, pp. 949–952 (2003)
22.
Lucks, M., Oki, N.: A radial basis function network (RBFN) for function approximation. In Proceedings of the 42nd Midwest symposium on circuits and systems 2, pp. 1099–1101 (1999)
23.
Masih, A., Masih, R.: Dynamic modeling of stock market interdependencies: An empirical investigation of Australia and the Asian NICs. Review of Pacific Basin Financial Markets and Policies, 4 (2), pp. 235–264 (2001)CrossRef
24.
Poggio, F.: Regularization theory, radial basis functions and networks. In From statistics to neural networks: Theory and pattern recognition applications, pp. 83–104. NATO ASI Series.
25.
Proedrou, K., Nouretdinov, I., Vovk, V., Gammerman, A.: Transductive confidence machines for pattern recognition. In T. Elomaa, H. Mannila, H. Toivonen (Eds.), Lecture Notes in Computer Science, machine learning: ECML 2002, 2430, pp. 221–231. Springer Berlin/Heidelberg (2002)
26.
Psillaki, M., Margaritis, D.: Long-run interdependence and dynamic linkages in international stock markets: Evidence from France, Germany and the U.S. Journal of Money, Investment and Banking, 4, pp. 59–73. EuroJournals Publishing (2008)
27.
Rodrigues, P., Gama, J., Pedroso, J.: Hierarchical clustering of time-series data streams. IEEE Trans. on Knowl. and Data Eng., 20, pp. 615–627 (2008)CrossRef
28.
Song, Q., Kasabov, N.: ECM - a novel on-line, evolving clustering method and its applications. In M. Posner (Ed.), Foundations of cognitive science, pp. 631–682. The MIT Press, Massachusetts, USA (2001)
29.
Song, Q., Kasabov, N.: NFI: a neuro-fuzzy inference method for transductive reasoning. IEEE Transactions on Fuzzy Systems, 13 (6), pp. 799–808 (2005)CrossRef
30.
Soucy, P., Mineau, G.: A simple kNN algorithm for text categorization. In Proceedings IEEE international conference on data mining, ICDM 2001, pp. 647–649 (2001)CrossRef
31.
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15 (1), pp. 116–132 (1985)MATH
32.
Vapnik, V.: Statistical learning theory. Wiley-Interscience, Chichester (2008)
33.
Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Scholkopf, B.: Feature selection and transduction for prediction of molecular bioactivity for drug design Bioinformatics, 19 (6), pp. 764–771 (2003)
34.
Widiputra, H., Kho, H., Lukas, Pears, R., Kasabov, N.: A novel evolving clustering algorithm with polynomial regression for chaotic time-series prediction. In C. Leung, M. Lee, J. Chan (Eds.), Lecture Notes in Computer Science, neural information processing 5864, pp. 114–121. Springer Berlin/Heidelberg (2009)
35.
Widiputra, H., Pears, R., Kasabov, N.: Personalised modelling for multiple time-series data prediction: a preliminary investigation in Asia Pacific stock market indexes movement. In Proceedings of the 15th international conference on advances in neuro-information processing part I, ICONIP 2008, 5506, pp. 1237–1244. Springer, Berlin Heidelberg (2008)
36.
Widiputra, H., Pears, R., Kasabov, N.: Dynamic interaction networks versus local trend models for multiple time-series prediction. Cybernetics and Systems, 42, pp. 1–24 (2011)CrossRef
37.
Widiputra, H., Pears, R., Kasabov, N.: Multiple time-series prediction through multiple time-series relationships profiling and clustered recurring trends. In Proceedings of the pacific asia conference on knowledge discovery and data mining, PAKDD (2011)
38.
Wooldridge, J.: Introductory econometrics: a modern approach, 3rd edition. Cengage Learning Services, South Western College, Florence KY, USA (2005)
39.
Wu, D., Bennett, K., Cristianini, N., Shawe-Taylor, J.: Large margin trees for induction and transduction. In Proceedings of the sixteenth international conference on machine learning, ICML 1999, pp. 474–483. Morgan Kaufmann Publishers Inc., San Francisco, CA (1999)
40.
Yamada, T., Yamashita, K., Ishii, N., Iwata, K.: Text classification by combining different distance functions with weights. In Seventh ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, SNPD 2006, pp. 85–90 (2006)
41.
Yang, H., Chan, L., King, I.: Support vector machine regression for volatile stock market prediction. In Third international conference on intelligent data engineering and automated learning, IDEAL 2002, pp.391–396. Springer (2002)
42.
Zadeh, L.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man and Cybernetics, 3 (1), pp. 28–44 (1973)MathSciNetMATHCrossRef
Metadaten
Titel
Dynamic Learning of Multiple Time Series in a Nonstationary Environment
verfasst von
Harya Widiputra
Russel Pears
Nikola Kasabov
Copyright-Jahr
2012
Verlag
Springer New York
Buch
Learning in Non-Stationary Environments

Print ISBN: 978-1-4419-8019-9

Electronic ISBN: 978-1-4419-8020-5

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-1-4419-8020-5_12

Premium Partner

    Bildnachweise