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Time-Series Forecasting
A01=Chris Chatfield
advanced time series forecasting methods
ARIMA Class
ARIMA Model
ARMA Model
Author_Chris Chatfield
Category=KCH
Category=KF
Category=PBT
cointegration modeling
Conditional Expectation
economic data analysis
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
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error
exponential
Exponential Smoothing
Forecast Accuracy
Forecast Errors
Forecasting Methods
GARCH Model
interval
Kalman Lter
methods
MMSE Forecast
model
multivariate analysis
neural network prediction
Observation Errors
out-of
Out-of Sample Forecast Errors
Out-of Sample Forecasts
point
Point Forecasts
prediction
prediction interval calculation
Rst Dierences
sample
SARIMA Model
smoothing
Smoothing Parameter
State Space Models
statistical forecasting techniques
Tar Model
Time Plot
Time Series Model
Transfer Function Model
Var Model
VARMA Models
Product details
- ISBN 9781584880639
- Dimensions: 156 x 234mm
- Publication Date: 25 Oct 2000
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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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.
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