Evaluation Study of Linear Combination Technique for SVM related Time Series Forecasting

dc.contributor.author Cheng, Xian
dc.contributor.author Wu, Ji
dc.contributor.author Xu, Jin
dc.date.accessioned 2019-01-02T23:50:34Z
dc.date.available 2019-01-02T23:50:34Z
dc.date.issued 2019-01-08
dc.description.abstract Time series forecasting and SVM are widely used in many domains, for example, smart city and digital services. Focusing on SVM related time series forecasting model, in this paper we empirical investigate the performance of eight linear combination techniques by using M3 competition dataset which includes 3003 time series. The results reveals that the “forecast combination puzzle” is not exist for combining SVM related forecasting model as the simple average is almost the worst combination technique.
dc.format.extent 7 pages
dc.identifier.doi 10.24251/HICSS.2019.151
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59563
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Decision Support for Smart Cities
dc.subject Decision Analytics, Mobile Services, and Service Science
dc.subject Time Series Forecasting, SVM, Linear Combination, M3 Competition
dc.title Evaluation Study of Linear Combination Technique for SVM related Time Series Forecasting
dc.type Conference Paper
dc.type.dcmi Text
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