Poster : Visual Prediction of Time Series

Publikationstyp: Beitrag zu einer Konferenz
URI (zitierfähiger Link): http://nbn-resolving.de/urn:nbn:de:bsz:352-192027
Autor/innen: Hao, Ming; Janetzko, Halldor; Sharma, Ratnesh; Dayal, Umeshwar; Keim, Daniel; Castellanos, Malu
Erscheinungsjahr: 2009
Konferenz: 2009 IEEE Symposium on Visual Analytics Science and Technology, 12. Okt 2009 - 13. Okt 2009, Atlantic City, NJ, USA
Erschienen in: 2009 IEEE Symposium on Visual Analytics Science and Technology. - IEEE, 2009. - S. 229-230. - ISBN 978-1-4244-5283-5
DOI (zitierfähiger Link): https://dx.doi.org/10.1109/VAST.2009.5333420
Zusammenfassung:
Many well-known time series prediction methods have been used daily by analysts making decisions. To reach a good prediction, we introduce several new visual analysis techniques of smoothing, multi-scaling, and weighted average with the involvement of human expert knowledge. We combine them into a well-fitted method to perform prediction. We have applied this approach to predict resource consumption in data center for next day planning.
Fachgebiet (DDC): 004 Informatik
Link zur Lizenz: Nutzungsbedingungen
Universitätsbibliographie: Ja

Zitieren

Dateien zu dieser Ressource

[PDF]
Name: Hao_Visual prediction.pdf
Größe: 664.8Kb
Format: PDF
Öffnen
Prüfsumme: MD5:1960c3d024fa58409725ba9423d1b405

HAO, Ming, Halldor JANETZKO, Ratnesh SHARMA, Umeshwar DAYAL, Daniel KEIM, Malu CASTELLANOS, 2009. Poster : Visual Prediction of Time Series. 2009 IEEE Symposium on Visual Analytics Science and Technology. Atlantic City, NJ, USA, 12. Okt 2009 - 13. Okt 2009. In: 2009 IEEE Symposium on Visual Analytics Science and Technology. IEEE, pp. 229-230. ISBN 978-1-4244-5283-5. Available under: doi: 10.1109/VAST.2009.5333420

@inproceedings{Hao2009-10Poste-19202, title={Poster : Visual Prediction of Time Series}, year={2009}, doi={10.1109/VAST.2009.5333420}, isbn={978-1-4244-5283-5}, publisher={IEEE}, booktitle={2009 IEEE Symposium on Visual Analytics Science and Technology}, pages={229--230}, author={Hao, Ming and Janetzko, Halldor and Sharma, Ratnesh and Dayal, Umeshwar and Keim, Daniel and Castellanos, Malu} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/19202"> <dcterms:bibliographicCitation>First publ. in: IEEE Symposium on Visual Analytics Science and Technology : VAST 2009 ; 12-13 Oct 2009, Atlantic City, NJ / Institute of Electrical and Electronics Engineers. - Piscataway, NJ : IEEE Service Center, 2009. - pp. 229-231. - ISBN 978-1-4244-5283-5</dcterms:bibliographicCitation> <dcterms:issued>2009-10</dcterms:issued> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-05-03T09:38:21Z</dcterms:available> <dc:creator>Sharma, Ratnesh</dc:creator> <dc:contributor>Dayal, Umeshwar</dc:contributor> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dcterms:title>Poster : Visual Prediction of Time Series</dcterms:title> <dcterms:abstract xml:lang="deu">Many well-known time series prediction methods have been used daily by analysts making decisions. To reach a good prediction, we introduce several new visual analysis techniques of smoothing, multi-scaling, and weighted average with the involvement of human expert knowledge. We combine them into a well-fitted method to perform prediction. We have applied this approach to predict resource consumption in data center for next day planning.</dcterms:abstract> <dc:creator>Janetzko, Halldor</dc:creator> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/19202/2/Hao_Visual%20prediction.pdf"/> <dc:contributor>Castellanos, Malu</dc:contributor> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/19202/2/Hao_Visual%20prediction.pdf"/> <dc:creator>Hao, Ming</dc:creator> <dc:contributor>Hao, Ming</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-05-03T09:38:21Z</dc:date> <dc:language>eng</dc:language> <dc:rights>terms-of-use</dc:rights> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/19202"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dcterms:rights rdf:resource="https://kops.uni-konstanz.de/page/termsofuse"/> <dc:contributor>Sharma, Ratnesh</dc:contributor> <dc:creator>Dayal, Umeshwar</dc:creator> <dc:contributor>Janetzko, Halldor</dc:contributor> <dc:creator>Keim, Daniel</dc:creator> <dc:contributor>Keim, Daniel</dc:contributor> <dc:creator>Castellanos, Malu</dc:creator> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

Hao_Visual prediction.pdf 160

Das Dokument erscheint in:

KOPS Suche


Stöbern

Mein Benutzerkonto