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Paper Details
Paper Title
Mining Multivariate Temporal Patterns for Event Characterization and Prediction
Authors
  G.Aswini,  A.R.Ashok Kumar,  D.Durai kumar
Abstract
Characteristic and prediction of the events are essential in many applications, such as forecasting economic growth, financial decision making etc. This can be done by processing the temporal patterns which are observed event data sequence often closely related to certain time-ordered structures. Among several existing method reconstructed phase space work well but only for univariate data sequence. So we propose a multivariate reconstructed phase space which is uses supervised clustering for characteristic and prediction of event from these dynamic data sequence. An optimization method is applied finally to estimate the parameters of the classifier that defines an optimal decision boundary in the Multivariate RPS.
Keywords- Temporal Patterns, Reconstructed Phse Space, Uni-variate Data, Multivariate RPS.
Publication Details
Unique Identification Number - IJEDR1501082Page Number(s) - 449-452Pubished in - Volume 3 | Issue 1 | Jan 2015DOI (Digital Object Identifier) -    Yadav StreetPublisher - IJEDR (ISSN - 2321-9939)
Cite this Article
  G.Aswini,  A.R.Ashok Kumar,  D.Durai kumar,   "Mining Multivariate Temporal Patterns for Event Characterization and Prediction", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.3, Issue 1, pp.449-452, Jan 2015, Available at :http://www.ijedr.org/papers/IJEDR1501082.pdf
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