A Similarity-Based Approach for the All-Time Demand Prediction of New Automotive Spare Parts

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dc.contributor.authorSteuer, Daniel
dc.contributor.authorHutterer, Verena
dc.contributor.authorKorevaar, Peter
dc.contributor.authorFromm, Hansjoerg
dc.date.accessioned2017-12-28T00:50:10Z
dc.date.available2017-12-28T00:50:10Z
dc.date.issued2018-01-03
dc.format.extent8 pages
dc.identifier.doi10.24251/HICSS.2018.191
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50078
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectService Analytics
dc.subject0
dc.titleA Similarity-Based Approach for the All-Time Demand Prediction of New Automotive Spare Parts
dc.typeConference Paper
dc.type.dcmiText

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