Data mining in time series databases (World Scientific, 2004)
Includes bibliographical references
Segmenting time series : a survey and novel approach - E. Keogh [and others] -- - A survey of recent methods for efficient retrieval of similar time sequences - M.L. Hetland -- - Indexing of compressed time series - E. Fink and K.B. Pratt -- - Indexing time-series under conditions of noise - M. Vlachos, D. Gunopulos, and G. Das -- - Change detection in classification models induced from time series data - G. Zeira [and others] -- - Classification and detection of abnormal events in time series of graphs - H. Bunke and M. Kraetzl -- - Boosting interval-based literals : variable length and early classification - C.J. Alonso González and J.J. Rodríguez Diez -- - Median strings : a review - X. Jiang, H. Bunke, and J. Csirik
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 manual examines 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 text 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