Introduction to time series analysis and forecasting / / Douglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci

• Autore: Montgomery Douglas C.
• Pubblicazione: Hoboken, New Jersey : , : Wiley, , 2015; 2015
• Edizione: Second edition.
• Descrizione fisica: 1 online resource (671 p.)
• Disciplina: 519.55
• Soggetto topico: Forecasting; Time-series analysis
• Persona (resp. second.): JenningsCheryl L.; KulahciMurat
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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 -- 5.3 Finite Order Moving Average Processes -- 5.3.1 The First-Order Moving Average Process, MA(1) -- 5.3.2 The Second-Order Moving Average Process, MA(2) -- 5.4 Finite Order Autoregressive Processes -- 5.4.1 First-Order Autoregressive Process, AR(1) -- 5.4.2 Second-Order Autoregressive Process, AR(2) -- 5.4.3 General Autoregressive Process, AR() -- 5.4.4 Partial Autocorrelation Function, PACF -- 5.5 Mixed Autoregressive-Moving Average Processes -- 5.5.1 Stationarity of ARMA(p, q) Process -- 5.5.2 Invertibility of ARMA(p, q) Process -- 5.5.3 ACF and PACF of ARMA(p, q) Process -- 5.6 Nonstationary Processes -- 5.6.1 Some Examples of ARIMA(p, d, q) Processes -- 5.7 Time Series Model Building -- 5.7.1 Model Identification. ; 5.7.2 Parameter Estimation -- 5.7.3 Diagnostic Checking -- 5.7.4 Examples of Building ARIMA Models -- 5.8 Forecasting Arima Processes -- 5.9 Seasonal Processes -- 5.10 Arima Modeling of Biosurveillance Data -- 5.11 Final Comments -- 5.12 R Commands for Chapter 5 -- Exercises -- 6 Transfer Functions and Intervention Models -- 6.1 Introduction -- 6.2 Transfer Function Models -- 6.3 Transfer Function-Noise Models -- 6.4 Cross-Correlation Function -- 6.5 Model Specification -- 6.6 Forecasting with Transfer Function-Noise Models -- 6.7 Intervention Analysis -- 6.8 R Commands for Chapter 6 -- Exercises -- 7 Survey of Other Forecasting Methods -- 7.1 Multivariate Time Series Models and Forecasting -- 7.1.1 Multivariate Stationary Process -- 7.1.2 Vector ARIMA Models -- 7.1.3 Vector AR (VAR) Models -- 7.2 State Space Models -- 7.3 Arch and Garch Models -- 7.4 Direct Forecasting of Percentiles -- 7.5 Combining Forecasts to Improve Prediction Performance -- 7.6 Aggregation and Disaggregation of Forecasts -- 7.7 Neural Networks and Forecasting -- 7.8 Spectral Analysis -- 7.9 Bayesian Methods in Forecasting -- 7.10 Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures -- 7.11 R Commands for Chapter 7 -- Exercises -- APPENDIX A Statistical Tables -- APPENDIX B Data Sets for Exercises -- APPENDIX C Introduction to R -- BASIC CONCEPTS IN R -- Bibliography -- Index -- EULA.
• Titolo autorizzato: Introduction to time series analysis and forecasting
• ISBN: 1-118-74522-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910827811503321
• Lo trovi qui: Univ. Federico II
• Serie: Wiley series in probability and statistics.