File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Analysis of call centre arrival data using singular value decomposition

TitleAnalysis of call centre arrival data using singular value decomposition
Authors
KeywordsData reduction
Anomaly detection
Call centre
Feature extraction
Forecasting call volume
Singular value decomposition
Issue Date2005
Citation
Applied Stochastic Models in Business and Industry, 2005, v. 21, n. 3, p. 251-263 How to Cite?
AbstractWe consider the general problem of analysing and modelling call centre arrival data. A method is described for analysing such data using singular value decomposition (SVD). We illustrate that the outcome from the SVD can be used for data visualization, detection of anomalies (outliers), and extraction of significant features from noisy data. The SVD can also be employed as a data reduction tool. Its application usually results in a parsimonious representation of the original data without losing much information. We describe how one can use the reduced data for some further, more formal statistical analysis. For example, a short-term forecasting model for call volumes is developed, which is multiplicative with a time series component that depends on day of the week. We report empirical results from applying the proposed method to some real data collected at a call centre of a large-scale U.S. financial organization. Some issues about forecasting call volumes are also discussed. Copyright © 2005 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/219488
ISSN
2015 Impact Factor: 0.574
2015 SCImago Journal Rankings: 0.613

 

DC FieldValueLanguage
dc.contributor.authorShen, Haipeng-
dc.contributor.authorHuang, Jianhua Z.-
dc.date.accessioned2015-09-23T02:57:13Z-
dc.date.available2015-09-23T02:57:13Z-
dc.date.issued2005-
dc.identifier.citationApplied Stochastic Models in Business and Industry, 2005, v. 21, n. 3, p. 251-263-
dc.identifier.issn1524-1904-
dc.identifier.urihttp://hdl.handle.net/10722/219488-
dc.description.abstractWe consider the general problem of analysing and modelling call centre arrival data. A method is described for analysing such data using singular value decomposition (SVD). We illustrate that the outcome from the SVD can be used for data visualization, detection of anomalies (outliers), and extraction of significant features from noisy data. The SVD can also be employed as a data reduction tool. Its application usually results in a parsimonious representation of the original data without losing much information. We describe how one can use the reduced data for some further, more formal statistical analysis. For example, a short-term forecasting model for call volumes is developed, which is multiplicative with a time series component that depends on day of the week. We report empirical results from applying the proposed method to some real data collected at a call centre of a large-scale U.S. financial organization. Some issues about forecasting call volumes are also discussed. Copyright © 2005 John Wiley & Sons, Ltd.-
dc.languageeng-
dc.relation.ispartofApplied Stochastic Models in Business and Industry-
dc.subjectData reduction-
dc.subjectAnomaly detection-
dc.subjectCall centre-
dc.subjectFeature extraction-
dc.subjectForecasting call volume-
dc.subjectSingular value decomposition-
dc.titleAnalysis of call centre arrival data using singular value decomposition-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1002/asmb.598-
dc.identifier.scopuseid_2-s2.0-21244452429-
dc.identifier.volume21-
dc.identifier.issue3-
dc.identifier.spage251-
dc.identifier.epage263-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats