Adding the time dimension to real-world databases produces TimeSeries Databases (TSDB) and introduces new aspects and difficultiesto data mining and knowledge discovery. This book covers thestate-of-the-art methodology for mining time series databases. Thenovel data mining methods presented in the book include techniquesfor efficient segmentation, indexing, and classification of noisy anddynamic time series. A graph-based method for anomaly detection intime series is described and the book also studies the implicationsof a novel and potentially useful representation of time series asstrings. The problem of detecting changes in data mining models thatare induced from temporal databases is additionally discussed.

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