Introduction to time series analysis and forecasting/
Montgomery, Douglas C.
Introduction to time series analysis and forecasting/ Dauglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci - 2nd ed. - New Jersey: Wiley, c2016. - xiv, 643 p. : ill. ; 24 cm. - (Wiley series in probability and statistics) .
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.
9781118745113
Time-series analysis
Forecasting
Mathematics
519.55 / MON/I
Introduction to time series analysis and forecasting/ Dauglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci - 2nd ed. - New Jersey: Wiley, c2016. - xiv, 643 p. : ill. ; 24 cm. - (Wiley series in probability and statistics) .
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.
9781118745113
Time-series analysis
Forecasting
Mathematics
519.55 / MON/I