Adding the time dimension to real-world databases produces Time
Series Databases (TSDB) and introduces new aspects and difficulties
to data mining and knowledge discovery. This book covers the
state-of-the-art methodology for mining time series databases. The
novel data mining methods presented in the book include techniques
for efficient segmentation, indexing, and classification of noisy and
dynamic time series. A graph-based method for anomaly detection in
time series is described and the book also studies the implications
of a novel and potentially useful representation of time series as
strings. The problem of detecting changes in data mining models that
are induced from temporal databases is additionally discussed.