Time series clustering and classification/ Elizabeth Ann Maharaj, Pierpaolo D'Urso and Jorge Caiado
Series: Computer Science and Data Analysis SeriesPublication details: Boca Raton: CRC, 2019Description: xv, 228 pages, 24 cmISBN:- 9781498773218
- 23 000SA.3 M214
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.3 M214 (Browse shelf(Opens below)) | Available | 138500 |
Browsing ISI Library, Kolkata shelves Close shelf browser (Hides shelf browser)
| 000SA.3 In61 Modeling and stochastic learning for forecasting in high dimensions : | 000SA.3 K45 Robustness in statistical forecasting / | 000SA.3 L693 Time series with mixed spectra / | 000SA.3 M214 Time series clustering and classification/ | 000SA.3 M787 Introduction to time series analysis and forecasting / | 000SA.3 P171 Time series analysis / | 000SA.3 P372 Time series analysis, modeling and applications : |
Includes bibliographical references and index
1. Introduction -- 2. Time series features and models -- I Unsupervised Approaches: Clustering techniques for Time Series -- 3. Traditional cluster analysis -- 4. Fuzzy clustering -- 5. Observation-based clustering -- 6. Feature-based clustering -- 7. Model-based clustering -- 8. Other time series clustering approaches -- II Supervised Approaches: Classification techniques for Time Series -- 9. feature- based approaches -- 10. Other time series classification approaches -- III Software and Data Sets -- 11. Software and data sets -- Bibliography -- Subject index
Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students.
Features: Provides an overview of the methods and applications of pattern recognition of time series, Covers a wide range of techniques, including unsupervised and supervised approaches, Includes a range of real examples from medicine, finance, environmental science, and more, R and MATLAB code, and relevant data sets are available on a supplementary
There are no comments on this title.
