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Springer Texts in Statistics Ser.: Introduction to Time Series and...
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eBay item number:164330617651
Item specifics
- Condition
- ISBN
- 9780387953519
- Subject Area
- Computers, Business & Economics, Mathematics
- Publication Name
- Introduction to Time Series and Forecasting
- Publisher
- Springer
- Item Length
- 10.2 in
- Subject
- Mathematical & Statistical Software, Probability & Statistics / General, Probability & Statistics / Time Series, Statistics
- Publication Year
- 2010
- Series
- Springer Texts in Statistics Ser.
- Type
- Textbook
- Format
- Mixed Lot
- Language
- English
- Features
- Revised
- Item Weight
- 40.4 Oz
- Item Width
- 7.7 in
- Number of Pages
- Xiv, 437 Pages
About this product
Product Information
This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied in economics, engineering, and the natural and social sciences. The book assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This second edition contains detailed instructions on the use of the new totally windows-based computer package ITSM2000. Expanded treatments are also given of several topics treated only briefly in the first edition. These include regression with time series errors, which plays an important role in forecasting and inference, and ARCH and GARCH models, which are widely used for the modeling of financial time series. These models can be fitted using the new version of ITSM. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include the Burg and Hannan-Rissanen algorithms, unit roots, the EM algorithm, structural models, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introductions are also given to cointegration and to non-linear, continuous-time and long-memory models.
Product Identifiers
Publisher
Springer
ISBN-10
0387953515
ISBN-13
9780387953519
eBay Product ID (ePID)
109167422
Product Key Features
Number of Pages
Xiv, 437 Pages
Language
English
Publication Name
Introduction to Time Series and Forecasting
Publication Year
2010
Subject
Mathematical & Statistical Software, Probability & Statistics / General, Probability & Statistics / Time Series, Statistics
Features
Revised
Type
Textbook
Subject Area
Computers, Business & Economics, Mathematics
Series
Springer Texts in Statistics Ser.
Format
Mixed Lot
Dimensions
Item Weight
40.4 Oz
Item Length
10.2 in
Item Width
7.7 in
Additional Product Features
Edition Number
2
LCCN
2001-049262
Dewey Edition
21
Reviews
From the reviews:"The emphasis is on hands-on experience and the friendly software that accompanies the book serves the purpose admirably. ...The authors should be congratulated for making the subject accessible and fun to learn. The book is a pleasure to read and highly recommended. I regard it as the best introductory text in town." ISI Short Book Reviews, From the reviews: "The emphasis is on hands-on experience and the friendly software that accompanies the book serves the purpose admirably. ... The authors should be congratulated for making the subject accessible and fun to learn. The book is a pleasure to read and highly recommended. I regard it as the best introductory text in town." ISI Short Book Reviews
Target Audience
Scholarly & Professional
Number of Volumes
1 Vol.
Illustrated
Yes
Dewey Decimal
519.5/5
Edition Description
Revised Edition
Lc Classification Number
Qa76.75-76.765qa273
Table of Content
Preface 1 INTRODUCTION 1.1 Examples of Time Series 1.2 Objectives of Time Series Analysis 1.3 Some Simple Time Series Models 1.3.3 A General Approach to Time Series Modelling 1.4 Stationary Models and the Autocorrelation Function 1.4.1 The Sample Autocorrelation Function 1.4.2 A Model for the Lake Huron Data 1.5 Estimation and Elimination of Trend and Seasonal Components 1.5.1 Estimation and Elimination of Trend in the Absence of Seasonality 1.5.2 Estimation and Elimination of Both Trend and Seasonality 1.6 Testing the Estimated Noise Sequence 1.7 Problems 2 STATIONARY PROCESSES 2.1 Basic Properties 2.2 Linear Processes 2.3 Introduction to ARMA Processes 2.4 Properties of the Sample Mean and Autocorrelation Function 2.4.2 Estimation of $\gamma(\cdot)$ and $
ho(\cdot)$ 2.5 Forecasting Stationary Time Series 2.5.3 Prediction of a Stationary Process in Terms of Infinitely Many Past Values 2.6 The Wold Decomposition 1.7 Problems 3 ARMA MODELS 3.1 ARMA($p,q$) Processes 3.2 The ACF and PACF of an ARMA$(p,q)$ Process 3.2.1 Calculation of the ACVF 3.2.2 The Autocorrelation Function 3.2.3 The Partial Autocorrelation Function 3.3 Forecasting ARMA Processes 1.7 Problems 4 SPECTRAL ANALYSIS 4.1 Spectral Densities 4.2 The Periodogram 4.3 Time-Invariant Linear Filters 4.4 The Spectral Density of an ARMA Process 1.7 Problems 5 MODELLING AND PREDICTION WITH ARMA PROCESSES 5.1 Preliminary Estimation 5.1.1 Yule-Walker Estimation 5.1.3 The Innovations Algorithm 5.1.4 The Hannan-Rissanen Algorithm 5.2 Maximum Likelihood Estimation 5.3 Diagnostic Checking 5.3.1 The Graph of $ =1,\ldots,n\ 5.3.2 The Sample ACF of the Residuals 5.3.3 Tests for Randomness of the Residuals 5.4 Forecasting 5.5&
Copyright Date
2002
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eBay item number:164330617651
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