Toward unsupervised activity discovery using multi-dimensional motif detection in time series
Alireza Vahdatpour, Navid Amini, Majid Sarrafzadeh · 2009
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 ad-dress 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 evalua-tion has been done on several wearable systems that are used by real subjects. 1