Skip to content
Cart (0) Free shipping over $35*
Buybacks (0) Free shipping on buybacks
loading
Help Contact
Return rental ›

Introduction to Time Series and Forecasting

Best in textbook rentals since 2012!

ISBN-10: 0387953515

ISBN-13: 9780387953519

Edition: 2nd 2002 (Revised)

Authors: Peter J. Brockwell, Richard A. Davis

List price: $119.00
Blue ribbon 30 day, 100% satisfaction guarantee!
Rent eBooks
Marketplace
4 new & used from $6.75
what's this?
Rush Rewards U
Members Receive:
Carrot Coin icon
XP icon
You have reached 400 XP and carrot coins. That is the daily max!

Description:

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, the student version of which is included with the text. 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…    
Customers also bought

Book details

List price: $119.00
Edition: 2nd
Copyright year: 2002
Publisher: Springer
Publication date: 4/29/2010
Binding: Mixed Media
Pages: 437
Size: 8.25" wide x 9.25" long x 1.50" tall
Weight: 2.332

Preface
Introduction
Examples of Time Series
Objectives of Time Series Analysis
Some Simple Time Series Models
Some Zero-Mean Models
Models with Trend and Seasonality
A General Approach to Time Series Modeling
Stationary Models and the Autocorrelation Function
The Sample Autocorrelation Function
A Model for the Lake Huron Data
Estimation and Elimination of Trend and Seasonal Components
Estimation and Elimination of Trend in the Absence of Seasonality
Estimation and Elimination of Both Trend and Seasonality
Testing the Estimated Noise Sequence
Problems
Stationary Processes
Basic Properties
Linear Processes
Introduction to ARMA Processes
Properties of the Sample Mean and Autocorrelation Function
Estimation of [mu]
Estimation of [gamma]([middle dot]) and [rho]([middle dot])
Forecasting Stationary Time Series
The Durbin-Levinson Algorithm
The Innovations Algorithm
Prediction of a Stationary Process in Terms of Infinitely Many Past Values
The Wold Decomposition
Problems
ARMA Models
ARMA(p, q) Processes
The ACF and PACF of an ARMA(p, q) Process
Calculation of the ACVF
The Autocorrelation Function
The Partial Autocorrelation Function
Examples
Forecasting ARMA Processes
Problems
Spectral Analysis
Spectral Densities
The Periodogram
Time-Invariant Linear Filters
The Spectral Density of an ARMA Process
Problems
Modeling and Forecasting with ARMA Processes
Preliminary Estimation
Yule-Walker Estimation
Burg's Algorithm
The Innovations Algorithm
The Hannan-Rissanen Algorithm
Maximum Likelihood Estimation
Diagnostic Checking
The Graph of {R[subscript t], t = 1, ..., n{
The Sample ACF of the Residuals
Tests for Randomness of the Residuals
Forecasting
Order Selection
The FPE Criterion
The AICC Criterion
Problems
Nonstationary and Seasonal Time Series Models
ARIMA Models for Nonstationary Time Series
Identification Techniques
Unit Roots in Time Series Models
Unit Roots in Autoregressions
Unit Roots in Moving Averages
Forecasting ARIMA Models
The Forecast Function
Seasonal ARIMA Models
Forecasting SARIMA Processes
Regression with ARMA Errors
OLS and GLS Estimation
ML Estimation
Problems
Multivariate Time Series
Examples
Second-Order Properties of Multivariate Time Series
Estimation of the Mean and Covariance Function
Estimation of [mu]
Estimation of [Gamma](h)
Testing for Independence of Two Stationary Time Series
Bartlett's Formula
Multivariate ARMA Processes
The Covariance Matrix Function of a Causal ARMA Process
Best Linear Predictors of Second-Order Random Vectors
Modeling and Forecasting with Multivariate AR Processes
Estimation for Autoregressive Processes Using Whittle's Algorithm
Forecasting Multivariate Autoregressive Processes
Cointegration
Problems
State-Space Models
State-Space Representations
The Basic Structural Model
State-Space Representation of ARIMA Models
The Kalman Recursions
Estimation For State-Space Models
State-Space Models with Missing Observations
The EM Algorithm
Generalized State-Space Models
Parameter-Driven Models
Observation-Driven Models
Problems
Forecasting Techniques
The ARAR Algorithm
Memory Shortening
Fitting a Subset Autoregression
Forecasting
Application of the ARAR Algorithm
The Holt-Winters Algorithm
The Algorithm
Holt-Winters and ARIMA Forecasting
The Holt-Winters Seasonal Algorithm
The Algorithm
Holt-Winters Seasonal and ARIMA Forecasting
Choosing a Forecasting Algorithm
Problems
Further Topics
Transfer Function Models
Prediction Based on a Transfer Function Model
Intervention Analysis
Nonlinear Models
Deviations from Linearity
Chaotic Deterministic Sequences
Distinguishing Between White Noise and iid Sequences
Three Useful Classes of Nonlinear Models
Modeling Volatility
Continuous-Time Models
Long-Memory Models
Problems
Random Variables and Probability Distributions
Distribution Functions and Expectation
Random Vectors
The Multivariate Normal Distribution
Problems
Statistical Complements
Least Squares Estimation
The Gauss-Markov Theorem
Generalized Least Squares
Maximum Likelihood Estimation
Properties of Maximum Likelihood Estimators
Confidence Intervals
Large-Sample Confidence Regions
Hypothesis Testing
Error Probabilities
Large-Sample Tests Based on Confidence Regions
Mean Square Convergence
The Cauchy Criterion
An ITSM Tutorial
Getting Started
Running ITSM
Preparing Your Data for Modeling
Entering Data
Information
Filing Data
Plotting Data
Transforming Data
Finding a Model for Your Data
Autofit
The Sample ACF and PACF
Entering a Model
Preliminary Estimation
The AICC Statistic
Changing Your Model
Maximum Likelihood Estimation
Optimization Results
Testing Your Model
Plotting the Residuals
ACF/PACF of the Residuals
Testing for Randomness of the Residuals
Prediction
Forecast Criteria
Forecast Results
Model Properties
ARMA Models
Model ACF, PACF
Model Representations
Generating Realizations of a Random Series
Spectral Properties
Multivariate Time Series
References
Index
  • TextbookRush.com BBB Business Review
  • Trustpilot
Like TextbookRush on Facebook Follow TextbookRush on X Follow TextbookRush on Instagram Subscribe to TextbookRush on YouTube Follow TextbookRush on Pinterest Add TextbookRush on Snapchat Add TextbookRush on TikTok
© 2002-2024 TextbookRush
Subscribe