Forecasting with exponential smoothing
Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. Initially, a big attraction was the limited requirements for computer storage. More importantly today, the equations in exponential smoothing methods...
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2012
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oai:scholar.dlu.edu.vn:DLU123456789-305692012-04-30T22:37:13Z Forecasting with exponential smoothing Hyndman, Rob J. Koehler, Anne B. Ord, J. Keith Snyder, Ralph D. Econometrics Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. Initially, a big attraction was the limited requirements for computer storage. More importantly today, the equations in exponential smoothing methods for estimating the parameters and generating the forecasts are very intuitive and easy to understand. As a result, these methods have been widely implemented in business applications. However, a shortcoming of exponential smoothing has been the lack of a statistical framework that produces both prediction intervals and point forecasts. The innovations state space approach provides this framework while retaining the intuitive nature of exponential smoothing in its measurement and state equations. It provides prediction intervals, maximum likelihood estimation, procedures for model selection, and much more. 2012-04-27T07:42:56Z 2012-04-27T07:42:56Z 2008 Book 3540719164 http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/30569 en application/pdf Springer |
| institution |
Thư viện Trường Đại học Đà Lạt |
| collection |
Thư viện số |
| language |
English |
| topic |
Econometrics |
| spellingShingle |
Econometrics Hyndman, Rob J. Koehler, Anne B. Ord, J. Keith Snyder, Ralph D. Forecasting with exponential smoothing |
| description |
Exponential smoothing methods have been around since the 1950s, and are
still the most popular forecasting methods used in business and industry.
Initially, a big attraction was the limited requirements for computer storage.
More importantly today, the equations in exponential smoothing methods
for estimating the parameters and generating the forecasts are very intuitive
and easy to understand. As a result, these methods have been widely
implemented in business applications.
However, a shortcoming of exponential smoothing has been the lack of a
statistical framework that produces both prediction intervals and point forecasts.
The innovations state space approach provides this framework while
retaining the intuitive nature of exponential smoothing in its measurement
and state equations. It provides prediction intervals, maximum likelihood
estimation, procedures for model selection, and much more. |
| format |
Book |
| author |
Hyndman, Rob J. Koehler, Anne B. Ord, J. Keith Snyder, Ralph D. |
| author_facet |
Hyndman, Rob J. Koehler, Anne B. Ord, J. Keith Snyder, Ralph D. |
| author_sort |
Hyndman, Rob J. |
| title |
Forecasting with exponential smoothing |
| title_short |
Forecasting with exponential smoothing |
| title_full |
Forecasting with exponential smoothing |
| title_fullStr |
Forecasting with exponential smoothing |
| title_full_unstemmed |
Forecasting with exponential smoothing |
| title_sort |
forecasting with exponential smoothing |
| publisher |
Springer |
| publishDate |
2012 |
| url |
http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/30569 |
| _version_ |
1757653827029303296 |