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Chatfield C. Time-Series Forecasting

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Chatfield C. Time-Series Forecasting
Chapman & Hall/CRC, 2000. – 265 p. – ISBN: 1584880635, 9781584880639
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.
Abbreviations and Notation
Types of forecasting method
Some preliminary questions
The dangers of extrapolation
Are forecasts genuinely out-of-sample?
Brief overview of relevant literature
Basics of Time-Series Analysis
Different types of time series
Objectives of time-series analysis
Simple descriptive techniques
Stationary stochastic processes
Some classes of univariate time-series model
The correlogram
Univariate Time-Series Modelling
ARIMA models and related topics
State space models
Growth curve models
Non-linear models
Time-series model building
Univariate Forecasting Methods
The prediction problem
Model-based forecasting
Ad hoc forecasting methods
Some interrelationships and combinations
Multivariate Forecasting Methods
Single-equation models
Vector AR and ARMA models
Econometric models
Other approaches
Some relationships between models
A Comparative Assessment of ForecastingMethods
Criteria for choosing a forecasting method
Measuring forecast accuracy
Forecasting competitions and case studies
Choosing an appropriate forecasting method
CalculatingI nterval Forecasts
The need for different approaches
Expected mean square prediction error
Procedures for calculating P.I.s
A comparative assessment
Why are P.I.s too narrow?
An example
Summary and recommendations
Model Uncertainty and Forecast Accuracy
Introduction to model uncertainty
Model building and data dredging
Inference after model selection: Some findings
Coping with model uncertainty
Summary and discussion
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