Abstract
Reasoning effectively about time-varying data requires sophisticated pattern detection mechanisms. This paper describes techniques developed for detecting patterns in time-varying data with the ultimate aim of generating textual descriptions of the data. Preliminary experiments are described in which the visually significant features in weather data are extracted and compared against hand-written expert descriptions.
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© 1997 Springer-Verlag
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Boyd, S. (1997). Detecting and describing patterns in time-varying data using wavelets. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052873
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DOI: https://doi.org/10.1007/BFb0052873
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