Introduction to Time Series Analysis and Forecasting [Elektronisk resurs].

• Cover; Series Page; Title Page; Copyright; Preface; CHAPTER 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 RESOURCES FOR FORECASTING; CHAPTER 2: Statistics Background for Forecasting; 2.1 INTRODUCTION; 2.2 GRAPHICAL DISPLAYS; 2.3 NUMERICAL DESCRIPTION OF TIME SERIES DATA; 2.4 USE OF DATA TRANSFORMATIONS AND ADJUSTMENTS; 2.5 GENERAL APPROACH TO TIME SERIES MODELING AND FORECASTING; 2.6 EVALUATING AND MONITORING FORECASTING MODEL PERFORMANCE; CHAPTER 3: Regression Analysis and Forecasting; 3.1 INTRODUCTION. 
• 3.2 LEAST SQUARES ESTIMATION IN LINEAR REGRESSION MODELS3.3 STATISTICAL INFERENCE IN LINEAR REGRESSION; 3.4 PREDICTION OF NEW OBSERVATIONS; 3.5 MODEL ADEQUACY CHECKING; 3.6 VARIABLE SELECTION METHODS IN REGRESSION; 3.7 GENERALIZED AND WEIGHTED LEAST SQUARES; 3.8 REGRESSION MODELS FOR GENERAL TIME SERIES DATA; CHAPTER 4: Exponential Smoothing Methods; 4.1 INTRODUCTION; 4.2 FIRST-ORDER EXPONENTIAL SMOOTHING; 4.3 MODELING TIME SERIES DATA; 4.4 SECOND-ORDER EXPONENTIAL SMOOTHING; 4.5 HIGHER-ORDER EXPONENTIAL SMOOTHING; 4.6 FORECASTING; 4.7 EXPONENTIAL SMOOTHING FOR SEASONAL DATA. 
• 4.8 EXPONENTIAL SMOOTHERS AND ARIMA MODELSCHAPTER 5: Autoregressive Integrated Moving Average (ARIMA) Models; 5.1 INTRODUCTION; 5.2 LINEAR MODELS FOR STATIONARY TIME SERIES; 5.3 FINITE ORDER MOVING AVERAGE (MA) PROCESSES; 5.4 FINITE ORDER AUTOREGRESSIVE PROCESSES; 5.5 MIXED AUTOREGRESSIVE-MOVING AVERAGE (ARMA) PROCESSES; 5.6 NONSTATIONARY PROCESSES; 5.7 TIME SERIES MODEL BUILDING; 5.8 FORECASTING ARIMA PROCESSES; 5.9 SEASONAL PROCESSES; 5.10 FINAL COMMENTS; CHAPTER 6: Transfer Functions and Intervention Models; 6.1 INTRODUCTION; 6.2 TRANSFER FUNCTION MODELS; 6.3 TRANSFER FUNCTION-NOISE MODELS. 
• 6.4 CROSS CORRELATION FUNCTION6.5 MODEL SPECIFICATION; 6.6 FORECASTING WITH TRANSFER FUNCTION-NOISE MODELS; 6.7 INTERVENTION ANALYSIS; CHAPTER 7: Survey of Other Forecasting Methods; 7.1 MULTIVARIATE TIME SERIES MODELS AND FORECASTING; 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 SOME COMMENTS ON PRACTICAL IMPLEMENTATION AND USE OF STATISTICAL FORECASTING PROCEDURES; APPENDIX A: Statistical Tables. 
• APPENDIX B: Data Sets for ExercisesBibliography; Index; WILEY SERIES IN PROBABILITY AND STATISTICS;. 

• An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis b 

Ämnesord

Forecasting. 

Time-series analysis. 

Genre

Electronic books.  (LCSH)

Klassifikation

519.55 (DDC)

Thie (kssb/8 (machine generated))