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Forecasting with exponential smoothing by Anne B. Koehler, J. Keith Ord, Ralph D. Snyder, Rob Hyndman

Forecasting with exponential smoothing Anne B. Koehler, J. Keith Ord, Ralph D. Snyder, Rob Hyndman ebook
Format: pdf
Publisher: Springer
ISBN: 3540719164, 9783540719168
Page: 356

For short-range forecasts of mature products; Exponential Smoothing; this method is useful to forecast demand when no linear trend is in the data. Forecasts will change with new each observation, but depending on the alpha factor of your exponential smoothing (e.g. Linear Smoothing; similar to method 9, a formula is assigned to give weights. A good choice is to use simple exponential smoothing or a moving average as the naïve. Is there any way to calculate confidence intervals for such prognosis (ex-ante)? For univariate forecasting, you can use Exponential smoothing models, intervention models, Box-Jenkins models, distributed lag models, vector autoregression models etc. Covariates) in exponential smoothing (ETS) models, and I have done it a couple of times in my published work. The forecast can be calculated for one or more steps (time intervals). In tests of the exponential smoothing method, using 1,001 times series during the M1 competition, it was discovered that taking a simple average improved accuracy compared to the best individual forecast. I'm using exponential smoothing (Brown's method) for forecasting. In recent years, with the rapid development of science and technology, economy and society have made great progress, meanwhile a large mount of date such as agricultural prices have been produced in various fields. I have thought quite a lot about including regressors (i.e. Unlike regression methods exponential smoothing does not depend on autocorrelation so if the autocorrelation value is pretty high, the forecast cannot be valid. Off-the-shelf products like Forecast Pro or Autobox.