I have different univariate time series and the goal is to detect outliers automatically. Therefore I used different algorithms for different time series. But the first step would be to detect automatically the time series class. Attached you could see different univariate time series (continuous1, binary, continuous2).
HOW could I distinguish between these?
I have already tried kmeans with some simple generated features but it doesn't work. The first main goal should be to distinguish between binary and continuous data.
Best Answer
First of all clustering of time-series with usual clustering algorithm tends to give improber results, which is demonstrated quite clearly in this paper. However, you could aim for clustering describing parts of time-series; like seasonal frequency, trend, cycle and so on. This time-series specific attributes themselves can be clustered and therefore may help you in finding "similiar" ts. A R-Package with a good time-series clustering function is timetk.