4 ms·

EDIT: Sorry for the wall of text. Also, why your idea is good: Consider a person going from "sitting" to "jogging". When the change point occurs, the reading o

• rvlasveld 13y ago
  Thank you for your nice reply; currently I don't have time to look at your suggestions in depth, but I can give a (short) notion of my interpretation of 'change detection' in this context. I will consider a signal from accelerometers in a smartphone. There are already many algorithms to determine the activity an user is currently performing (such as walking, jogging, biking, sitting, etc). Often these methods take windows of time (1-2 seconds) and compare characteristics with learned models. This way the time series data is clustered/classified into models. An additional result from this is an (implicit) segmentation of the time series arises. My theory is that when the segmentation of the time series is first performed explicitly, the (bigger) chucks of data can be better characterized. So my goal is to find the change points in the time series; and a change points means that the underlying process (being the user carrying the phone) has changed (e.g. from walking to running). I want to detect the change point using novelty-detection techniques; so roughly speaking: when a certain number of data points are considered "new"/"outlier", I assume there is a change in the signal and report that moment of time. Then the model adapts to the new distribution of data and watches again for a change point.