Data Mining In Time Series Databases
| Author | : | |
| Rating | : | 4.59 (905 Votes) |
| Asin | : | B0102CR5QW |
| Format Type | : | paperback |
| Number of Pages | : | 204 Pages |
| Publish Date | : | 2017-03-18 |
| Language | : | English |
DESCRIPTION:
"An unremarkable selection of research articles" according to Dimitri Shvorob. This thin book presents eight academic papers discussing handling of sequences. I did not find any of them interesting on its own or good as a survey, but academics doing research in machine learning may disagree. If you are one, you most likely can get the original papers. If you are a practitioner, pass without a second thought.
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 novel data mining methods presented in the book include techniques for efficient segmentation, indexing, and classification of noisy and dynamic time series. 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. The problem of detecting changes in data mining models that are induced from temporal databases is additionally discussed.. This book covers the state-of-the-art methodology for mining time series databases