# Introduction to Time Series and Forecasting [With CDROM] / Edition 2

ISBN-10:
0387953515
ISBN-13:
9780387953519
Pub. Date:
04/28/2010
Publisher:
Springer New York

## Other Format - Rent for

View All Available Formats & Editions
Select a Purchase Option (2nd ed. 2002, Corr. 9th printing 2010)
• purchase options
$29.77$119.00 Save 75%
• Free return shipping at the end of the rental period details
• Textbook Rentals in 3 Easy Steps  details
Note: Access code and/or supplemental material are not guaranteed to be included with textbook rental or used textbook.
• purchase options
$76.77$119.00 Save 35% Current price is $76.77, Original price is$119. You Save 35%.
• purchase options
$65.25$119.00 Save 45% Current price is $65.25, Original price is$119. You Save 45%.
Note: Access code and/or supplemental material are not guaranteed to be included with textbook rental or used textbook.
• purchase options

## Overview

Introduction to Time Series and Forecasting [With CDROM] / Edition 2

This is an introduction to time series that emphasizes methods and analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills. Statisticians and students will learn the latest methods in time series and forecasting, along with modern computational models and algorithms.

## Product Details

ISBN-13: 9780387953519 Springer New York 04/28/2010 Springer Texts in Statistics Series 2nd ed. 2002, Corr. 9th printing 2010 469 7.68(w) x 10.24(h) x 0.06(d)

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 $\rho(\cdot)$
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 \$\t=1,\ldots,n\
5.3.2 The Sample ACF of the Residuals

Average Review