000			a
999			_c198779 _d198779
020			_a9781118745113
040			_cCUS
082			_a519.55 _bMON/I
100			_aMontgomery, Douglas C.
245			_aIntroduction to time series analysis and forecasting/ _cDauglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci
250			_a2nd ed.
260			_aNew Jersey: _bWiley, _cc2016.
300			_axiv, 643 p. : _bill. ; _c24 cm.
440			_a(Wiley series in probability and statistics)
505			_aPreface -- 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.
650			_aTime-series analysis
650			_aForecasting
650			_aMathematics
700			_aJennings, Cheryl L.
700			_aKulahci, Murat
942			_cWB16