Bayesian Modelling of the Temporal Aspects of Smart Home Activity with Circular Statistics

Tom Diethe, Niall Twomey, Peter Flach

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

6 Citations (Scopus)
32 Downloads (Pure)

Abstract

Typically, when analysing patterns of activity in a smart home environment, the daily patterns of activity are either ignored completely or summarised into a high-level “hour-of-day” feature that is then combined with sensor activities. However, when summarising the temporal nature of an activity into a coarse feature such as this, not only is information lost after discretisation, but also the strength of the periodicity of the action is ignored. We propose to model the temporal nature of activities using circular statistics, and in particular by performing Bayesian inference with Wrapped Normal (WN) and WN Mixture (WNM) models. We firstly demonstrate the accuracy of inference on toy data using both Gibbs sampling and Expectation Propagation (EP), and then show the results of the inference on publicly available smart-home data. Such models can be useful for analysis or prediction in their own right, or can be readily combined with larger models incorporating multiple modalities of sensor activity.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II
EditorsAnnalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares
PublisherSpringer Verlag
Pages279-294
Number of pages16
ISBN (Electronic)9783319235257
ISBN (Print)9783319235240
DOIs
Publication statusPublished - 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
Volume9285
ISSN (Print)0302-9743

Structured keywords

  • Jean Golding
  • SPHERE

Keywords

  • circular statistics
  • Bayesian analysis
  • bayesian model selection

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