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Introduction to statistical time series by Wayne A. Fuller

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Published by Wiley in New York .
Written in English

Subjects:

  • Time-series analysis.,
  • Regression analysis.

Book details:

Edition Notes

StatementWayne A. Fuller.
SeriesWiley series in probability and statistics
Classifications
LC ClassificationsQA280 .F84 1996
The Physical Object
Paginationxxii, 698 p. :
Number of Pages698
ID Numbers
Open LibraryOL782901M
ISBN 100471552399
LC Control Number95014875

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Overall I find Introduction to Time Series and Forecasting to be a very useful and enlightening introduction to time series. –Journal of the American Statistical Association The emphasis is on hands-on experience and the friendly software that accompanies the . As part of the prestigious Wiley Series in Probability and Statistics, this book provides a lucid introduction to the field and, in this new Second Edition, covers the important advances of recent years, including nonstationary models, nonlinear estimation, multivariate models, state space representations, and empirical model identification. The subject of time series is of considerable interest, especiallyamong researchers in econometrics, engineering, and the naturalsciences. As part of the prestigious Wiley Series in Probabilityand Statistics, this book provides a lucid introduction to thefield and, in this new Second Edition, covers the importantadvances of recent years, including nonstationary models, .   A much-needed introduction to the field of discrete-valued time series, with a focus on count-data time series. Time series analysis is an essential tool in a wide array of fields, including business, economics, computer science, epidemiology, finance, manufacturing and meteorology, to name just a few.

Time series modeling and forecasting has fundamental importance to various practical domains. Thus a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and effeciency of time series modeling and by: For those new to Stata, the book begins with a mild yet fast-paced introduction to Stata, highlighting all the features you need to know to get started using Stata for time-series analysis. Before diving into analysis of time series, Becketti includes a quick refresher on statistical foundations such as regression and hypothesis testing. The subject of time series is of considerable interest, especiallyamong researchers in econometrics, engineering, and the naturalsciences. As part of the prestigious Wiley Series in Probabilityand Statistics, this book provides a lucid introduction to thefield and, in this new Second Edition, covers the importantadvances of recent years, including nonstationary models,/5(3). 1. Introduction 1. Examples of Time Series 1 Objectives of Time Series Analysis 6 Some Simple Time Series Models 7 Some Zero-Mean Models 8 Models with Trend and Seasonality 9 A General Approach to Time Series Modeling 14 Stationary Models and the Autocorrelation Function 15 The Sample Cited by: 9.

Introduction to Statistical Analysis of Time Series Richard A. Davis Department of Statistics. 2 Time Series: A collection of observations x t, each one being recorded at time t. (Time could be discrete, t = 1,2,3,, or continuous t > 0.) Objective of Time Series Analaysis Data compression-provide compact description of the data. Explanatory File Size: KB. Get this from a library! Introduction to statistical time series. [Wayne A Fuller] -- The book provides an introduction to representations for statistical time series and to the estimation of time series models. Both the time domain and frequency domain approaches are developed. The. Box, Jenkins - Time Series Analysis: Forecasting and Control Probably most famous book dedicated to time series, from two pioneers of modelling time series. It should be stressed that their work and book is not solely focused on economics, which is a serious limitation for using this book as introductory textbook. 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.