Exponential smoothing！
Although exponential smoothing methods have been around since the 1950s,
a modeling framework incorporating stochastic models, likelihood calculations,
prediction intervals, and procedures for model selection was not
developed until relatively recently, with the work of ? and ?. In these (and
other) papers, a class of state spacemodels has been developed that underlies
all of the exponential smoothing methods.
In this chapter, we provide an introduction to the ideas underlying exponential
smoothing and the associated state space models.Many of the details
will be skipped over in this chapter, but will be covered in later chapters.



Forecast！
This article presents a comparison of forecasting performance for a variety of linear and nonlinear time series models using the U.S. unemployment rate. Our main emphases are on measuring forecasting performance during economic expansions and contractions by exploiting the asymmetric cyclical behavior of unemployment numbers, on building vector models that incorporate initial jobless claims as a leading indicator, and on utilizing additional information provided by the monthly rate for forecasting the quarterly rate. Comparisons are also made with the consensus forecasts from the Survey of Professional Forecasters. In addition, the forecasts of nonlinear models are combined with the consensus forecasts. The results show that significant improvements in forecasting accuracy can be obtained over existing methods.