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Time Series Modelling Unobserved by Pelagatti Matteo (31 results)
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Time Series Modelling With Unobserved Components
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Time Series Modelling With Unobserved Components
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Time Series Modelling with Unobserved Components
Published by Chapman and Hall/CRC 2021-06, 2021
ISBN 10: 1032098430 ISBN 13: 9781032098432
Language: English
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Time Series Modelling with Unobserved Components
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ISBN 10: 1032098430 ISBN 13: 9781032098432
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Time Series Modelling with Unobserved Components
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Time Series Modelling with Unobserved Components
Published by Chapman and Hall/CRC 2015-08-03, 2015
ISBN 10: 148222500X ISBN 13: 9781482225006
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Time Series Modelling With Unobserved Components
Published by Chapman and Hall/CRC, 2015
ISBN 10: 148222500X ISBN 13: 9781482225006
Language: English
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Time Series Modelling with Unobserved Components
Published by Chapman and Hall/CRC, 2015
ISBN 10: 148222500X ISBN 13: 9781482225006
Language: English
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Time Series Modelling With Unobserved Components
Published by Chapman and Hall/CRC, 2015
ISBN 10: 148222500X ISBN 13: 9781482225006
Language: English
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Applied Time Series Modelling with Unobserved Components
Published by Taylor & Francis Group, 2015
ISBN 10: 148222500X ISBN 13: 9781482225006
Language: English
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Time Series Modelling With Unobserved Components
Published by Chapman and Hall/CRC, 2015
ISBN 10: 148222500X ISBN 13: 9781482225006
Language: English
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Time Series Modelling With Unobserved Components
Published by Chapman and Hall/CRC, 2015
ISBN 10: 148222500X ISBN 13: 9781482225006
Language: English
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Applied Time Series Modelling with Unobserved Components
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Language: English
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Applied Time Series Modelling with Unobserved Components
Published by Taylor & Francis Group, 2015
ISBN 10: 148222500X ISBN 13: 9781482225006
Language: English
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Time Series Modelling with Unobserved Components
Published by Chapman And Hall/CRC Jun 2021, 2021
ISBN 10: 1032098430 ISBN 13: 9781032098432
Language: English
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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.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.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.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.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. 276 pp. Englisch.
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Time Series Modelling with Unobserved Components
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Paperback. Condition: Brand New. 275 pages. 9.21x6.14x0.67 inches. In Stock. This item is printed on demand.
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Time Series Modelling with Unobserved Components
Published by Chapman And Hall/CRC, 2021
ISBN 10: 1032098430 ISBN 13: 9781032098432
Language: English
£ 59.78
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.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.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.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.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.
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£ 50.73
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Matteo M. PelagattiDespite 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 .
