Scaling up dynamic time warping to massive dataset

There has been much recent interest in adapting data mining algorithms t time series databases. Many of these algorithms need to compare time series Typically some variation or extension of Euclidean distance is used. However, as w demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive. In thi paper we introduce a modification of DTW which operates on a higher level abstraction of the data, in particular, a piecewise linear representation. We demonstrate that our approach allows us to outperform DTW by one to three orders o magnitude. We experimentally evaluate our approach on medical, astronomical and sign language data.