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

Chinellato, Eris ORCID: https://orcid.org/0000-0003-1920-2238, Mardia, Kanti V., Hogg, David C. and Cohn, Anthony G. (2017) An incremental von mises mixture framework for modelling human activity streaming data. Proceedings ITISE 2017. Granada, 18-20, September, 2017. In: International Work-Conference on Time Series Analysis (ITISE 2017), 18-20 Sept 2017, Granada, Spain. ISBN 9788417293017. [Conference or Workshop Item]

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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.

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