Introduction to time series analysis and forecasting with applications in SAS and SPSS by Robert A. Yaffee

Cover of: Introduction to time series analysis and forecasting | Robert A. Yaffee

Published by Academic Press in San Diego .

Written in English

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Subjects:

  • SAS (Computer file),
  • SPSS (Computer file),
  • Social sciences -- Statistical methods.,
  • Social sciences -- Statistical methods -- Computer programs.,
  • Social sciences -- Forecasting -- Computer programs.,
  • Social prediction -- Computer programs.,
  • Time-series analysis -- Computer programs.

Edition Notes

Includes bibliographical references and index.

Book details

Other titlesTime series analysis and forecasting :
StatementRobert A. Yaffee with Monnie McGee.
ContributionsMcGee, Monnie.
Classifications
LC ClassificationsHA30.3 .Y34 2000
The Physical Object
Paginationxxv, 528 p. :
Number of Pages528
ID Numbers
Open LibraryOL53674M
ISBN 100127678700
LC Control Number99062662

Download Introduction to time series analysis and forecasting

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Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts. Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upper-undergraduate and graduate-levels courses in forecasting and time series.

The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts/5(16). Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to /5(16).

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If you are unfamiliar, there is File Size: KB. Neural Nets in Time Series Analysis. Lastly, neural nets are used for anomaly detection and forecasting in time series analysis and are particularly useful when there are non-linear relationships to be discovered or when data has missing values or when lags aren’t regular in duration or length between events such as outliers.

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Email: [email protected] This is a simple introduction to time series analysis using the R statistics Size: KB. Download the eBook Introduction to Time Series Analysis and Forecasting in PDF or EPUB format and read it directly on your mobile phone, computer or any device. This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis.

The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear systems, state-space models, the Kalman filters. The correct bibliographic citation for this manual is as follows: Brocklebank, John C., David A.

Dickey, and Bong S. Choi. SAS® for Forecasting Time Series. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis.

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