Time series modelling with unobserved components (Record no. 390443)

MARC details
000 -LEADER
fixed length control field 03954cam a2200181 i 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 150910s2016 flua b 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781482225006
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.55
Item number P3T4
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Pelagatti, Matteo M.
9 (RLIN) 333117
245 10 - TITLE STATEMENT
Title Time series modelling with unobserved components
Statement of responsibility, etc. Pelagatti, Matteo M.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Boca Raton
Name of publisher, distributor, etc. CRC Press
Date of publication, distribution, etc. 2016
300 ## - PHYSICAL DESCRIPTION
Extent xvii, 257 p.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Table of Contents:<br/><br/>I. STATISTICAL PREDICTION AND TIME <br/><br/>i. Statistical Prediction<br/>ii. Optimal predictor<br/>iii. Optimal linear predictor<br/>iv. Linear models and joint normality<br/>v. Time Series Concepts<br/>vi. Definitions<br/>vii. Stationary processes<br/>viii. Integrated processes<br/>ix. ARIMA models<br/>x. Multivariate extensions<br/><br/>II. UNOBSERVED COMPONENTS<br/><br/>i. Unobserved Components Model<br/>ii. The unobserved components model<br/>iii. Trend<br/>iv. Cycle<br/>v. Seasonality<br/>vi. Regressors and Interventions<br/>vii. Static regression<br/>viii. Regressors in components and dynamic regression<br/>ix. Regression with time-varying coefficients<br/>x. Estimation<br/>xi. The state space form<br/>xii. Models in state space form<br/>xiii. Inference for the unobserved components<br/>xiv. Inference for the unknown parameters<br/>xv. Modelling<br/>xvi. Transforms<br/>xvii. Choosing the components<br/>xviii. State space form and estimation<br/>xix. Diagnostics checks, outliers and structural breaks<br/>xx. Model selection<br/>xxi. Multivariate Models<br/>xxii. Trends<br/>xxiii. Cycles<br/>xxiv. Seasonalities<br/>xxv. State space form and parametrisation<br/><br/>III. APPLICATIONS<br/><br/>i. Business Cycle Analysis with UCM<br/>ii. Introduction to the spectral analysis of time series<br/>iii. Extracting the business cycle from one time series<br/>iv. Extracting the business cycle from a pool of time series<br/>v. Case Studies<br/>vi. Impact of the point system on road injuries in Italy<br/>vii. An example of benchmarking: Building monthly GDP data<br/>viii. Hourly electricity demand<br/>ix. Software for UCM<br/>x. Software with ready-to-use UCM procedures<br/>xi. Software for generic models in state space form<br/><br/><br/>
520 ## - SUMMARY, ETC.
Summary, etc. Despite the unobserved components model (UCM) having many advantages over more popular forecasting techniques based on regression analysis, exponential smoothing, and ARIMA, the UCM is not well known among practitioners outside the academic community. Time Series Modelling with Unobserved Components rectifies this deficiency by giving a practical overview of the UCM approach, covering some theoretical details, several applications, and the software for implementing UCMs.<br/><br/>The book’s first part discusses introductory time series and prediction theory. Unlike most other books on time series, this text includes a chapter on prediction at the beginning because the problem of predicting is not limited to the field of time series analysis.<br/><br/>The second part introduces the UCM, the state space form, and related algorithms. It also provides practical modeling strategies to build and select the UCM that best fits the needs of time series analysts.<br/><br/>The third part presents real-world applications, with a chapter focusing on business cycle analysis and the construction of band-pass filters using UCMs. The book also reviews software packages that offer ready-to-use procedures for UCMs as well as systems popular among statisticians and econometricians that allow general estimation of models in state space form.<br/><br/>This book demonstrates the numerous benefits of using UCMs to model time series data. UCMs are simple to specify, their results are easy to visualize and communicate to non-specialists, and their forecasting performance is competitive. Moreover, various types of outliers can easily be identified, missing values are effortlessly managed, and working contemporaneously with time series observed at different frequencies poses no problem.<br/><br/><br/>(https://www.crcpress.com/Time-Series-Modelling-with-Unobserved-Components/Pelagatti/p/book/9781482225006)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Time-series analysis
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Missing observations - Statistics
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Total Renewals Full call number Barcode Date last seen Date last checked out Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Non-fiction Ahmedabad Ahmedabad   30/05/2016 5 5165.27 2 3 519.55 P3T4 192177 21/02/2020 08/12/2019 6456.59 30/05/2016 Book

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