Toward Unsupervised Activity Discovery Using Multi-Dimensional Motif Detection in Time Series
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1261-1265.
Abstract
This paper addresses the problem of activity and event discovery in multi dimensional time series data by proposing a novel method for locating multi dimensional motifs in time series. While recent work has been done in finding single dimensional and multi dimensional motifs in time series, we address motifs in general case, where the elements of multi dimensional motifs have temporal, length, and frequency variations. The proposed method is validated by synthetic data, and empirical evaluation has been done on several wearable systems that are used by real subjects.Alireza Vahdatpour, Navid Amini, Majid Sarrafzadeh
@inproceedings{ijcai2009p212,
title = {Toward Unsupervised Activity Discovery Using Multi-Dimensional Motif Detection in Time Series},
author = {Vahdatpour, Alireza and Amini, Navid and Sarrafzadeh, Majid},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1261--1265},
year = {2009},
url = {https://ijcai.org/proceedings/2009/212},
}
TY - CPAPER TI - Toward Unsupervised Activity Discovery Using Multi-Dimensional Motif Detection in Time Series AU - Vahdatpour, Alireza AU - Amini, Navid AU - Sarrafzadeh, Majid T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1261 EP - 1265 UR - https://ijcai.org/proceedings/2009/212 ER -Copied
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