nach oben

Soft Computing

Erschienen in:

01.04.2015 | Methodologies and Application

Description and classification of granular time series

verfasst von: Rami Al-Hmouz, Witold Pedrycz, Abdullah Balamash, Ali Morfeq

Erschienen in: Soft Computing | Ausgabe 4/2015

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

The study is concerned with a concept and a design of granular time series and granular classifiers. In contrast to the plethora of various models of time series, which are predominantly numeric, we propose to effectively exploit the idea of information granules in the description and classification of time series. The numeric (optimization-oriented) and interpretation abilities of granular time series and their classifiers are highlighted and quantified. A general topology of the granular classifier involving a formation of a granular feature space and the usage of the framework of relational structures (relational equations) in the realization of the classifiers is presented. A detailed design process is elaborated on along with a discussion of the pertinent optimization mechanisms. A series of experiments is covered leading to a quantitative assessment of the granular classifiers and their parametric analysis.

Vorheriger Artikel Abductive inference in Bayesian networks using distributed overlapping swarm intelligence

Nächster Artikel A genetic interval type-2 fuzzy logic-based approach for generating interpretable linguistic models for the brain P300 phenomena recorded via brain–computer interfaces

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 "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

Abonyi J, Feil B, Nemeth S, Arva P (2005) Modified Gath–Gevaclustering for fuzzy segmentation of multivariate time-series. Fuzzy Sets Syst 149(1):39–56CrossRefMATHMathSciNet

Al-Hmouz R, Pedrycz W, Balamash A, Morfeq A (2013) Granular representation schemes of time series: a study in an optimal allocation of information granularity. In: 2013 IEEE symposium on foundations of, computational intelligence (FOCI). pp 44–51

Aznarte JL, Benitez JM (2010) Equivalences between neural-autoregressive time series models and fuzzy systems. IEEE Trans Fuzzy Syst 21(8):1434–1444

Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New YorkCrossRefMATH

Bezdek JC (1994) What is computational intelligence. In: Zurada JM, Marks II, Robinson CJ (eds) Computational intelligence imitating life. IEEE Press, Piscataway, pp 1–12

Bezdek JC (1992) On the relationship between neural networks, pattern recognition and intelligence. Int J Approx Reason 6(2):85–107CrossRef

Carlisle A, Dozier G (2001) An off-the-shelf PSO. In: Proceedings of the 2001 workshop on particle swarm optimization. pp 1–6

Castillo O, Melin P (2007) Comparison of hybrid intelligent systems, neural networks and interval type-2 fuzzy logic for time series prediction. In: International Joint Conference on Neural Networks IJCNN. pp 3086–3091

Chen SM (2011) TAIEX Forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans Fuzzy Syst 19(1):1–12CrossRef

Das G, Lin K-I, Mannila H, Renganathan G, Smyth P (1998) Rule discovery from time series. Knowledge discovery in databases (KDD). AAAI Press, New York, pp 16–22

Frank J, Mannor S, Pineau J, Precup D (2013) Time series analysis using geometric template matching. IEEE Trans Patt Anal Mach Intell 35(2):740–754CrossRef

Fu LC, Chung FL, Ng CM (2006) Financial time series segmentation based on specialized binary tree representation. In: Proceedings of the 2006 international conference on data mining. pp 3–9

Fu TC (2011) A review on time series data mining. Eng Appl Artif Intell 24:164–181CrossRef

Hirano S, Tsumoto S (2002) Mining similar temporal patterns in long time-series data and its application to medicine. In: Proceedings of the 2002 IEEE international conference on data mining. pp 219–225

Jiang J, Zhang Z, Wang H (2007) A New segmentation algorithm to stock time series based on PIP approach. In: Proceedings of the international conference on wireless communications, networking and mobile computing. pp 5609–5612

Juang CF, Ksuan-Chun Ku (2005) A recurrent fuzzy network for fuzzy temporal sequence processing and gesture recognition. IEEE Trans Syst Man Cybern Part B 35(3):646–658CrossRef

Kamijo K, Yamanouchi A (2007) Signal processing using fuzzy fractal dimension and grade of fractality application to fluctuations in seawater temperature. In: IEEE symposium on computational intelligence in image and signal processing. pp 133–138

Kasabov NK, Song Q (2002) DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst 10(2):144–154CrossRef

Keogh E (2006) Weka on time series available: www.cs.ucr.edu/~eamonn/time_series_data/WekaOnTimeSeries.xls

Keogh E, Zhu Q, Hu B, Hao Y, Xi X, Wei L, Ratanamahatana CA (2011) The UCR time series classification/clustering. available: www.cs.ucr.edu/~eamonn/time_series_data/

Lin J, Keogh E, Lonardi S, Lankford JP, Nystrom DM (2004) Visually mining and monitoring massive time series. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining. pp 460–469

Minyoung K (2014) Probabilistic sequence translation-alignment model for time-series classification. IEEE Trans Knowl Data Eng 26(2):426–437CrossRef

Pedrycz W (1997) Computational intelligence: an introduction. CRC Press, Boca RatonMATH

Pedrycz W, Gacek A (2001) Learning of fuzzy automata. Int J Comput Intell Appl 1:19–33CrossRef

Pedrycz W, Valente de Oliveira J (2008) A development of fuzzy encoding and decoding through fuzzy clustering. IEEE Trans Instrum Measure 57(3):829–837CrossRef

Pedrycz W, Gomide F (2007) Fuzzy systems engineering: toward human–centric computing. Wiley, HobokenCrossRef

Perkins TJ, Hallett MT (2010) A trade-off between sample complexity and computational complexity in learning boolean networks from time-series data. IEEE/ACM Trans Comput Biol Bioinfo 7(1):118–125CrossRef

Tambouratzis T, Pazsit I (2009) General regression artificial neural networks for two-phase flow regime identification. In: Proceedings of international joint conference on neural networks IJCNN. pp 3082–3087

Woei W, Chek L, Teck W (2007) Type-2 fuzzy system for ECG arrhythmic classification. In: IEEE International Fuzzy Systems Conference FUZZ-IEEE. pp 1–6

Titel: Description and classification of granular time series
verfasst von: Rami Al-Hmouz
Witold Pedrycz
Abdullah Balamash
Ali Morfeq
Publikationsdatum: 01.04.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 4/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1311-z

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+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 4/2015

Optimal design of electromagnet for Maglev vehicles using hybrid optimization algorithm

Preface

A decision rule-based soft computing model for supporting financial performance improvement of the banking industry

Solving fuzzy Fredholm linear integral equations using Sinc method and double exponential transformation

Abductive inference in Bayesian networks using distributed overlapping swarm intelligence

Design of sliding-mode control based on fuzzy disturbance observer for minimization of switching gain and chattering

Premium Partner