nach oben

Neural Computing and Applications

Erschienen in:

01.11.2015 | Original Article

Predictive clustering on non-successive observations for multi-step ahead chaotic time series prediction

verfasst von: V. A. Gromov, E. A. Borisenko

Erschienen in: Neural Computing and Applications | Ausgabe 8/2015

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Predictive clustering algorithm based upon modified Wishart clustering technique is applied to predict chaotic time series. Concept of predictable and non-predictable observations is introduced in order to distinguish between reliable and unreliable predictions and, consequently, to enhance an ability to predict up to considerable number of positions ahead. Non-predictable observations are easily ascertained in the frameworks of predictive clustering, regardless used clustering technique. Clustering vectors are composed from observations according to set of patterns of non-successive positions in order to reveal characteristic observations sequences, useful for multi-step ahead predictions. The employed clustering method is featured with an ability to generate just enough clusters (submodels) to cope with inherent complexity of the series in question. The methods demonstrate good prediction quality for Lorenz system time series and satisfactory results for weather, energy market and financial time series.

Vorheriger Artikel A fuzzy back-propagation network approach for planning actions to shorten the cycle time of a job in dynamic random access memory manufacturing

Nächster Artikel Spiking neural network-based target tracking control for autonomous mobile robots

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

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

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+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

Barkley JR (2000) From catastrophe to chaos: a general theory of economic discontinuities, 2nd edn. Kluwer, Boston

Barnett WA, Deissenberg C, Feichtinger G (eds) (2004) Economic complexity: non-linear dynamics, multi-agents economies and learning. Elsevier, Amsterdam

Murray JD (2002) Mathematical biology 1: an introduction. Springer, BerlinMATH

Reichl LE (2004) The transition to chaos. Conservative classical systems and quantum manifestations. Springer, New YorkMATH

Konishi S, Kitagava G (2008) Information criteria and statistical modeling. Springer, New YorkMATHCrossRef

Palit AK, Popovic D (2005) Computational intelligence in time series forecasting. Theory and engineering applications. Springer, LondonMATH

Wang J, Chi D, Wu J, Lu H (2011) Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting. Expert Syst Appl 38:8419–8429CrossRef

Chen D, Wang J, Zou F, Yuan W, Hou W (2014) Time series prediction with improved neuro-endocrine model. Neural Comput Appl 24:1465–1475CrossRef

Gan M, Peng H, Peng X, Chen X, Inoussa G (2010) A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling. Inf Sci 180:4370–4383MathSciNetCrossRef

10.

Mirzaee H (2009) Linear combination rule in genetic algorithm for optimization of finite impulse response neural network to predict natural chaotic time series. Chaos, Solitons Fractals 41:2681–2689CrossRef

11.

Ghazali R, Hussain AJ, Liatsis P, Tawfik H (2008) The application of ridge polynomial neural network to multi-step ahead financial time series prediction. Neural Comput Appl 17:311–323CrossRef

12.

Abiyev RH (2011) Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction. Neural Comput Appl 20:249–259CrossRef

13.

Gholipour A, Lucas C, Mirmomeni AM, Shafiee M (2007) Extracting the main patterns of natural time series for long-term neurofuzzy prediction. Neural Comput Appl 16:383–393CrossRef

14.

Gu H, Wang H (2007) Fuzzy prediction of chaotic time series based on singular value decomposition. Appl Math Comput 185:1171–1185MATHCrossRef

15.

Singh P, Borah B (2013) High-order fuzzy-neuro expert system for time series forecasting. Knowl Based Syst 46:12–21CrossRef

16.

Hong WC (2010) Application of chaotic ant swarm optimization in electric load forecasting. Energy Policy 38:5830–5839CrossRef

17.

Niu D, Wang Y, Wu DD (2010) Power load forecasting using support vector machine and ant colony optimization. Expert Syst Appl 37:2531–2539CrossRef

18.

Pan Y, Jiang JC, Wang R, Jiang JJ (2011) Predicting the net heat of combustion of organic compounds from molecular structures based on ant colony optimization. J Loss Prev Process Ind 24:85–89CrossRef

19.

Martınez-Alvarez F, Troncoso A, Riquelme JC, Riquelme JM (2011) Energy time series forecasting based on pattern sequence similarity. IEEE Trans Knowl Data Eng 23(8):1230–1243CrossRef

20.

Blockeel H, De Raedt L, Ramon J (1998) Top-down induction of clustering trees. In: 15th international conference on machine learning, pp 55–63

21.

Dzeroski S, Gjorgjioski V, Slavkov I, Struyf J (2006) Analysis of time series data with predictive clustering trees. In: Proceedings of the 5th international conference on knowledge discovery in inductive databases, pp 63–80

22.

Martınez-Alvarez F, Troncoso A, Riquelme JC, Riquelme JM (2007) Partitioning-Clustering Techniques Applied to the Electricity Price Time Series. In: Intelligent data engineering and automated learning—IDEAL-2007, Springer, Berlin

23.

Gromov VA, Shulga AN (2012) Chaotic time series prediction with employment of ant colony optimization. Expert Syst Appl 39:8474–8478CrossRef

24.

Widiputra H, Kho H, Pears R, Kasabov N (2009) A novel evolving clustering algorithm with polynomial regression for chaotic time-series prediction. Neural Inf Process 5864:114–121CrossRef

25.

Widiputra H, Pears R, Kasabov N (2011) Multiple time-series prediction through multiple time-series relationships profiling and clustered recurring trends. Adv Knowl Discov Data Min 6635:161–172CrossRef

26.

Phu L, Anh DT (2011) Motif-based method for initialization the K-means clustering for time series data. In: AI 2011: advances in artificial intelligence, vol 7106. Springer, Berlin, pp 11–20

27.

Kantz H, Schreiber T (2004) Nonlinear time series analysis. Cambridge University Press, CambridgeMATH

28.

Bock HH (1974) Automatic classification. Vandenhoeck and Rupert, Göttingen

29.

Lapko AV, Chentsov SV (2000) Non-parametric information processing systems. Nauka, Moscow (in Russian)

30.

Jackson EA (1985) The Lorenz system: I. The global structure of its stable manifolds. Phys Scr 32:469–475MATHCrossRef

31.

Malinetskii GG, Potapov AP (2002) Modern problems of non-linear dynamics. Editorial URSS, Moscow (in Russian)

Titel: Predictive clustering on non-successive observations for multi-step ahead chaotic time series prediction
verfasst von: V. A. Gromov
E. A. Borisenko
Publikationsdatum: 01.11.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 8/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-1845-8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 8/2015

Adaptive neural control with intercepted adaptation for time-delay saturated nonlinear systems

Spiking neural P systems with structural plasticity

Spiking neural network-based target tracking control for autonomous mobile robots

A grouping biogeography-based optimization for location area planning

Photovoltaic energy production forecast using support vector regression

Development of a sequential Artificial Neural Network for predicting river water levels based on Brahmaputra and Ganges water levels

Premium Partner