· 29. Which is NOT true regarding simple exponential smoothing? a. It forecasts the value of the time series in the next period b. It has a smoothing constant ranging between 0 and 1. c. It uses a weighted average of past time-series values. d. …
· Selected Answer: Company records Correct Answer: Company records Question 29 3 out of 3 points Which is not true regarding simple exponential smoothing? Answer Selected Answer: Correct Answer: If alpha ( ) equals one, the forecast will never change α
See the answer. See the answer See the answer done loading. Which of the following is not true for exponential smoothing? Multiple Choice. has minimal data storage requirements. smoothes random variations in the data. weights each historical value equally. has an easily altered weighting scheme. smoothes real variations in the data.
7.1 Simple exponential smoothing. The simplest of the exponentially smoothing methods is naturally called simple exponential smoothing (SES) 13. This method is suitable for forecasting data with no clear trend or seasonal pattern. For example, the data in Figure 7.1 do not display any clear trending behaviour or any seasonality. (There is a rise in the last few years, which might …
For any α α between 0 and 1, the weights attached to the observations decrease exponentially as we go back in time, hence the name “exponential smoothing.” If α α is small (i.e., close to 0), more weight is given to observations from the more distant past. If α α is large (i.e., close to 1), more weight is given to the more recent observations. For the extreme case where α = 1 α = 1, ^yT +1|T =yT y ^ T + 1 | T = y T, and the forecasts are equal to the naïve forecasts.
The simplest of the exponentially smoothing methods is naturally called simple exponential smoothing (SES) 13. This method is suitable for forecasting data with no clear trend or seasonal pattern. For example, the data in Figure 7.1 do not display any clear trending behaviour or any seasonality. (There is a rise in the last few years, which might suggest a trend. We will consider whether a trended method would be better for this series later in this chapter.) We have already considered the naïve and the average as possible methods for forecasting such data (Section 3.1 ).
Hence, the average method assumes that all observations are of equal importance , and gives them equal weights when generating forecasts. We often want something between these two extremes. For example, it may be sensible to attach larger weights to more recent observations than to observations from the distant past.
The component form of simple exponential smoothing is not particularly useful, but it will be the easiest form to use when we start adding other components.
The basic idea of this model is to assume that the future will be more or less the same as the (recent) past. The only pattern that this model will be able to learn from demand history is its level (you can learn about more complex models on my blog on www.supchains.com ).
An early form of exponential smoothing forecast was initially proposed by R.G. Brown in 1956. His equations were refined in 1957 by Charles C. Holt — a US engineer from MIT and the University of Chicago — in his paper “ Forecasting Trends and Seasonals by Exponentially Weighted Averages.”.
Holt & Winters proposed different exponential smoothing models (simple, double, and triple) that can also understand & project a trend or a seasonality. This ensemble of models is then quite robust to forecast any time series. And, as Holt and Winters already explained in 1960, these forecasts only require a modest use of computation power.
alpha is a ratio (or a percentage) of how much importance the model will allocate to the most recent observation compared to the importance of demand history.
You can make your own simple exponential smoothing in Excel ( here) or Python ( here ).
This simple exponential smoothing model is slightly smarter than the moving average model thanks to its smarter weighting of the historical demand observation. But it has many limitations:
Always be cautious when you initialize the different parameters of a model not to give it too much information about the future.