Abstract
With most approaches to pattern discovery in time series the notion of a pattern is defined a priori and then an algorithm for the efficient discovery of patterns is proposed. But finding the intrinsic patterns in a collection of time series may require a search for the best pattern representation, too. For one dataset it may be important to consider absolute points in time, for other datasets only the shapes may be of interest. With some datasets reoccurring subseries match the pattern closely and with others only loosely. We propose an MDL-based approach to search not only for patterns, but for the intrinsic pattern representation. The preliminary results of this unsupervised method are promising, because in the examined (supervised) datasets the identified representations led to patterns that discriminate between classes.
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Schweier, A., Höppner, F. (2014). Finding the Intrinsic Patterns in a Collection of Time Series. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds) Advances in Intelligent Data Analysis XIII. IDA 2014. Lecture Notes in Computer Science, vol 8819. Springer, Cham. https://doi.org/10.1007/978-3-319-12571-8_25
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DOI: https://doi.org/10.1007/978-3-319-12571-8_25
Publisher Name: Springer, Cham
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