Time Series Analysis: Univariate and Multivariate Methods (Classic Version), 2nd edition

  • William W.S. Wei

Time Series Analysis: Univariate and Multivariate Methods (Classic Version)

ISBN-13:  9780134995366

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Overview

With its broad coverage of methodology, this comprehensive book is a useful learning and reference tool for those in applied sciences where analysis and research of time series is useful. Its plentiful examples show the operational details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets illustrate the models and methods useful for analyzing, modeling, and forecasting data collected sequentially in time. The text also offers a balanced treatment between theory and applications.

 

Time Series Analysis is a thorough introduction to both time-domain and frequency-domain analyses of univariate and multivariate time series methods, with coverage of the most recently developed techniques in the field.

Table of contents

1: Overview

1.1 Introduction

1.2 Examples and Scope of This Book

 

2: Fundamental Concepts

2.1 Stochastic Processes

2.2 The Autocovariance and Autocorrelation Functions

2.3 The Partial Autocorrelation Function

2.4 White Noise Processes

2.5 Estimation of the Mean, Autocovariances, and Autocorrelations

2.5.1 Sample Mean

2.5.2 Sample Autocovariance Function

2.5.3 Sample Autocorrelation Function

2.5.4 Sample Partial Autocorrelation Function

2.6 Moving Average and Autoregressive Representations of Time Series Processes

2.7 Linear Difference Equations

 

3: Stationary Time Series Models

3.1 Autoregressive Processes

3.1.1 The First-Order Autoregressive AR(1) Process

3.1.2 The Second-Order Autoregressive AR(2) Process

3.1.3 The General pth-Order Autoregressive AR(p) Process

3.2 Moving Average Processes

3.2.1 The First-Order Moving Average MA(1) Process

3.2.2 The Second-Order Moving Average MA(2) Process

3.2.3 The General qth-Order Moving Average MA(q) Process

3.3 The Dual Relationship Between AR(p) and MA(q) Processes

3.4 Autoregressive Moving Average ARMA(p, q) Processes

3.4.1 The General Mixed ARMA(p, q) Process

3.4.2 The ARMA(1, 1) Process

 

4: Nonstationary Time Series Models

4.1 Nonstationarity in the Mean

4.1.1 Deterministic Trend Models

4.1.2 Stochastic Trend Models

Published by Pearson (March 14th 2018) - Copyright © 2019