Enhanced descriptions from Syndetics:

From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space modelling to multivariate methods and including recent arrivals, such as GARCH models, neural networks, and cointegrated models.

The author compares the more important methods in terms of their theoretical inter-relationships and their practical merits. He also considers two other general forecasting topics that have been somewhat neglected in the literature: the computation of prediction intervals and the effect of model uncertainty on forecast accuracy.

Although the search for a "best" method continues, it is now well established that no single method will outperform all other methods in all situations-the context is crucial. Time-Series Forecasting provides an outstanding reference source for the more generally applicable methods particularly useful to researchers and practitioners in forecasting in the areas of economics, government, industry, and commerce.

Table of contents provided by Syndetics

• Preface (p. ix)
• Abbreviations and Notation (p. xi)
• 1 Introduction (p. 1)
• 1.1 Types of forecasting method (p. 3)
• 1.2 Some preliminary questions (p. 5)
• 1.3 The dangers of extrapolation (p. 7)
• 1.4 Are forecasts genuinely out-of-sample? (p. 8)
• 1.5 Brief overview of relevant literature (p. 9)
• 2 Basics of Time-Series Analysis (p. 11)
• 2.1 Different types of time series (p. 11)
• 2.2 Objectives of time-series analysis (p. 12)
• 2.3 Simple descriptive techniques (p. 13)
• 2.4 Stationary stochastic processes (p. 24)
• 2.5 Some classes of univariate time-series model (p. 27)
• 2.6 The correlogram (p. 30)
• 3 Univariate Time-Series Modelling (p. 35)
• 3.1 ARIMA models and related topics (p. 35)
• 3.2 State space models (p. 49)
• 3.3 Growth curve models (p. 53)
• 3.4 Non-linear models (p. 54)
• 3.5 Time-series model building (p. 72)
• 4 Univariate Forecasting Methods (p. 85)
• 4.1 The prediction problem (p. 85)
• 4.2 Model-based forecasting (p. 88)
• 4.3 Ad hoc forecasting methods (p. 95)
• 4.4 Some interrelationships and combinations (p. 104)
• 5 Multivariate Forecasting Methods (p. 109)
• 5.1 Introduction (p. 109)
• 5.2 Single-equation models (p. 117)
• 5.3 Vector AR and ARMA models (p. 130)
• 5.4 Cointegration (p. 138)
• 5.5 Econometric models (p. 142)
• 5.6 Other approaches (p. 144)
• 5.7 Some relationships between models (p. 148)
• 6 A Comparative Assessment of Forecasting Methods (p. 151)
• 6.1 Introduction (p. 151)
• 6.2 Criteria for choosing a forecasting method (p. 152)
• 6.3 Measuring forecast accuracy (p. 152)
• 6.4 Forecasting competitions and case studies (p. 157)
• 6.5 Choosing an appropriate forecasting method (p. 168)
• 6.6 Summary (p. 178)
• 7 Calculating Interval Forecasts (p. 181)
• 7.1 Introduction (p. 181)
• 7.2 Notation (p. 185)
• 7.3 The need for different approaches (p. 186)
• 7.4 Expected mean square prediction error (p. 187)
• 7.5 Procedures for calculating P.I.s (p. 190)
• 7.6 A comparative assessment (p. 205)
• 7.7 Why are P.I.s too narrow? (p. 206)
• 7.8 An example (p. 211)
• 7.9 Summary and recommendations (p. 213)
• 8 Model Uncertainty and Forecast Accuracy (p. 215)
• 8.1 Introduction to model uncertainty (p. 215)
• 8.2 Model building and data dredging (p. 217)
• 8.3 Examples (p. 220)
• 8.4 Inference after model selection: Some findings (p. 228)
• 8.5 Coping with model uncertainty (p. 233)
• 8.6 Summary and discussion (p. 241)
• References (p. 245)
• Index (p. 263)

Author notes provided by Syndetics