An incremental von mises mixture framework for modelling human activity streaming data

Conference item


Chinellato, E., Mardia, K., Hogg, D. and Cohn, A. 2017. An incremental von mises mixture framework for modelling human activity streaming data. International Work-Conference on Time Series Analysis (ITISE 2017). Granada, Spain 18 - 20 Sep 2017 pp. 379-389
TitleAn incremental von mises mixture framework for modelling human activity streaming data
AuthorsChinellato, E., Mardia, K., Hogg, D. and Cohn, A.
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.

LanguageEnglish
ConferenceInternational Work-Conference on Time Series Analysis (ITISE 2017)
Page range379-389
ISBN
Hardcover9788417293017
Publication dates
Print18 Sep 2017
Publication process dates
Deposited08 Mar 2018
Accepted15 Jul 2017
Output statusPublished
Book titleProceedings ITISE 2017. Granada, 18-20, September, 2017
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