Introduction to Time Series Analysis and Forecasting

Detalles Bibliográficos
Autor principal: Montgomery, Douglas C.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2015.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Introduction to Time Series Analysis and Forecasting
  • Contents
  • Preface
  • 1 Introduction to Forecasting
  • 1.1 The Nature and Uses of Forecasts
  • 1.2 Some Examples of Time Series
  • 1.3 The Forecasting Process
  • 1.4 Data for Forecasting
  • 1.4.1 The Data Warehouse
  • 1.4.2 Data Cleaning
  • 1.4.3 Imputation
  • 1.5 Resources for Forecasting
  • Exercises
  • 2 Statistics Background for Forecasting
  • 2.1 Introduction
  • 2.2 Graphical Displays
  • 2.2.1 Time Series Plots
  • 2.2.2 Plotting Smoothed Data
  • 2.3 Numerical Description of Time Series Data
  • 2.3.1 Stationary Time Series
  • 2.3.2 Autocovariance and Autocorrelation Functions
  • 2.3.3 The Variogram
  • 2.4 Use of Data Transformations and Adjustments
  • 2.4.1 Transformations
  • 2.4.2 Trend and Seasonal Adjustments
  • 2.5 General Approach to Time Series Modeling and Forecasting
  • 2.6 Evaluating and Monitoring Forecasting Model Performance
  • 2.6.1 Forecasting Model Evaluation
  • 2.6.2 Choosing Between Competing Models
  • 2.6.3 Monitoring a Forecasting Model
  • 2.7 R Commands for Chapter 2
  • Exercises
  • 3 Regression Analysis and Forecasting
  • 3.1 Introduction
  • 3.2 Least Squares Estimation in Linear Regression Models
  • 3.3 Statistical Inference in Linear Regression
  • 3.3.1 Test for Significance of Regression
  • 3.3.2 Tests on Individual Regression Coefficients and Groups of Coefficients
  • 3.3.3 Confidence Intervals on Individual Regression Coefficients
  • 3.3.4 Confidence Intervals on the Mean Response
  • 3.4 Prediction of New Observations
  • 3.5 Model Adequacy Checking
  • 3.5.1 Residual Plots
  • 3.5.2 Scaled Residuals and PRESS
  • 3.5.3 Measures of Leverage and Influence
  • 3.6 Variable Selection Methods in Regression
  • 3.7 Generalized and Weighted Least Squares
  • 3.7.1 Generalized Least Squares
  • 3.7.2 Weighted Least Squares
  • 3.7.3 Discounted Least Squares
  • 3.8 Regression Models for General Time Series Data
  • 3.8.1 Detecting Autocorrelation: The Durbin-Watson Test
  • 3.8.2 Estimating the Parameters in Time Series Regression Models
  • 3.9 Econometric Models
  • 3.10 R Commands for Chapter 3
  • Exercises
  • 4 Exponential Smoothing Methods
  • 4.1 Introduction
  • 4.2 First-Order Exponential Smoothing
  • 4.2.1 The Initial Value,
  • 4.2.2 The Value of l
  • 4.3 Modeling Time Series Data
  • 4.4 Second-Order Exponential Smoothing
  • 4.5 Higher-Order Exponential Smoothing
  • 4.6 Forecasting
  • 4.6.1 Constant Process
  • 4.6.2 Linear Trend Process
  • 4.6.3 Estimation of
  • 4.6.4 Adaptive Updating of the Discount Factor
  • 4.6.5 Model Assessment
  • 4.7 Exponential Smoothing for Seasonal Data
  • 4.7.1 Additive Seasonal Model
  • 4.7.2 Multiplicative Seasonal Model
  • 4.8 Exponential Smoothing of Biosurveillance Data
  • 4.9 Exponential Smoothers and Arima Models
  • 4.10 R Commands for Chapter 4
  • Exercises
  • 5 Autoregressive Integrated Moving Average (ARIMA) Models
  • 5.1 Introduction
  • 5.2 Linear Models for Stationary Time Series
  • 5.2.1 Stationarity
  • 5.2.2 Stationary Time Series

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