  Time series classification is an application of particular interest with the
increase of data to monitor. Classical techniques for time series
classification rely on point-to-point distances. Recently, Bag-of-Words
approaches have been used in this context. Words are quantized versions of
simple features extracted from sliding windows. The SIFT framework has proved
efficient for image classification. In this paper, we design a time series
classification scheme that builds on the SIFT framework adapted to time series
to feed a Bag-of-Words. We then refine our method by studying the impact of
normalized Bag-of-Words, as well as densely extract point descriptors. Proposed
adjustements achieve better performance. The evaluation shows that our method
outperforms classical techniques in terms of classification.
