{"id":161152,"date":"2011-06-01T00:00:00","date_gmt":"2011-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/learning-time-based-presence-probabilities\/"},"modified":"2018-10-16T21:22:22","modified_gmt":"2018-10-17T04:22:22","slug":"learning-time-based-presence-probabilities","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-time-based-presence-probabilities\/","title":{"rendered":"Learning Time-Based Presence Probabilities"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Many potential pervasive computing applications could use predictions of when a person will be at a certain place. Using a survey and GPS data from 34 participants in 11 households, we develop and test algorithms for predicting when a person will be at home or away. We show that our participants\u2019 self-reported home\/away schedules are not very accurate, and we introduce a probabilistic home\/away schedule computed from observed GPS data. The computation includes smoothing and a soft schedule template. We show how the probabilistic schedule outperforms both the self-reported schedule and an algorithm based on driving time. We also show how to combine our algorithm with the best part of the drive time algorithm for a slight boost in performance.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Many potential pervasive computing applications could use predictions of when a person will be at a certain place. Using a survey and GPS data from 34 participants in 11 households, we develop and test algorithms for predicting when a person will be at home or away. We show that our participants\u2019 self-reported home\/away schedules are [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-161152","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-locale-en_us"],"msr_publishername":"Springer Verlag","msr_edition":"Pervasive 2011","msr_affiliation":"","msr_published_date":"2011-06-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"220282","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"Learning Time-Based Presence Probabilities.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2011\/06\/Learning-Time-Based-Presence-Probabilities.pdf","id":220282,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":220282,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2011\/06\/Learning-Time-Based-Presence-Probabilities.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"jckrumm","user_id":32203,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=jckrumm"},{"type":"user_nicename","value":"ajbrush","user_id":30886,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=ajbrush"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144633],"msr_project":[170664],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":170664,"post_title":"PreHeat: Controlling Home Heating With Occupancy Prediction","post_name":"preheat-controlling-home-heating-with-occupancy-prediction","post_type":"msr-project","post_date":"2011-03-02 12:43:04","post_modified":"2023-11-28 10:06:40","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/preheat-controlling-home-heating-with-occupancy-prediction\/","post_excerpt":"Home heating uses more energy than any other residential energy expenditure, making increasing the efficiency of home heating an important goal for saving money and protecting the environment. We have built a home-heating system, PreHeat, that automatically programs your thermostat based on when you are home. PreHeat\u2019s goal is to reduce the amount of time a household\u2019s thermostat needs to be on without compromising the comfort of household members. 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