Time Series Modelling with Unobserved Components (Record no. 69895)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02135nam a2200217Ia 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260630101259.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260630s9999||||xx |||||||||||||| ||und|| |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781482225006 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.55 PEL |
| Edition number | 23 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Pelagatti, Matteo M. |
| 245 #0 - TITLE STATEMENT | |
| Title | Time Series Modelling with Unobserved Components |
| Statement of responsibility, etc | / Matteo M. Pelagatti. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | First edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Boca Raton, Florida |
| Name of publisher, distributor, etc | : Chapman & Hall/CRC Press |
| Date of publication, distribution, etc | , 2015. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xv, 275 p. |
| Other physical details | : illustrations, tables |
| Dimensions | ; 24 cm. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Time Series Modelling with Unobserved Components provides a comprehensive introduction to the Unobserved Components Model (UCM) approach for analysing and forecasting time series data. Written by Matteo M. Pelagatti, the book bridges statistical theory and practical application, making advanced state-space modelling techniques accessible to students, researchers, economists, and data analysts. The book begins with the fundamentals of statistical prediction and classical time series concepts, including stationarity, ARIMA models, and forecasting principles. It then develops the UCM framework by explaining how observed time series can be decomposed into underlying components such as trend, seasonal variation, cyclical movements, and irregular fluctuations. The text introduces the state-space representation of these models and explains estimation using the Kalman filter and smoothing algorithms. The later chapters focus on practical modelling strategies, intervention analysis, regression with time-varying coefficients, missing observations, outlier detection, and real-world applications such as business cycle analysis and economic forecasting. The author also reviews statistical software packages that support UCM estimation, enabling readers to implement these techniques in practice. The book is especially valuable for postgraduate students and professionals in statistics, econometrics, finance, economics, engineering, and data science. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Forecasting. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Mathematical statistics. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | State-space methods. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Time-series analysis. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | VC Office Books |
No items available.
