Chinellato, E, Mardia, KV, Hogg, DC orcid.org/0000-0002-6125-9564 et al. (1 more author) (2017) An Incremental von Mises Mixture Framework for Modelling Human Activity Streaming Data. In: Valenzuela, O, Rojas, F, Pomares, H and Rojas, I, (eds.) Proceedings ITISE 2017. International Work-Conference on Time Series Analysis (ITISE 2017), 18-20 Sep 2017, Grenada, Spain. , pp. 379-389. ISBN 978-84-17293-01-7
Abstract
Modelling the time of occurrence of events from data streams is a challenging task, since the underlying distributions can be both cyclic and multimodal. Moreover, in order to avoid the indefinite growth of data storage, historical streaming data has to be represented only with model parameters, discarding the single values. In this work, we introduce an incremental framework for a mixture of circular von Mises distributions to model the time of occurrence of events. Applying our framework to the time of occurrence of human activities, we show that it is able to represent the relevant information of a cyclic data stream by storing only the distribution parameters, highlighting that its use can extend to a number of applications.
Metadata
Item Type: | Proceedings Paper |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EU - European Union FP7-ICT-600623 |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Aug 2017 11:05 |
Last Modified: | 10 Jul 2019 15:03 |
Published Version: | http://itise.ugr.es/2017/previous.php |
Status: | Published |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:120101 |