I need to find a specific pattern in a continual time series and then to classify it into one of two groups good, or bad. So, the first step would be some kind of search in the time series, but that also comes to classification.
My question is not related to the classification per se, but to 'preparation' to classify. Concretely, as this is a stream of data my first question would be how to determine the size of the window? And whether these windows should overlap and by how much? I'm afraid that the result will pretty much depend on the way I perform the windowing.
Another thing which bugs me is how to prepare the data,as it is not labeled. Would it make sence to split the series, cluster it and then learn on specific cluster. The other approach I have in mind is to create a couple of samples manually, by splitting on windows which represent 'nice' examples and then perform some kind of bootstraping where I classify the stream,identify pattern and add that window to the training set. Which approach would be better? And is there any other way?