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Practical Predictive Analytics by Ralph Winters

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Selecting a smoothing factor

Sometimes selecting an appropriate smoothing factor is done via your own experience with the data, and let's you express your own view about how you expect the future to behave. For example if you thank that the data has recently changed to reflect new pattern, you might want to assume that the recent data is more important and use a smoothing factor close to 1. On the other hand, if you think that recent activity is just due to random fluctuations, you might want to choose a lower smoothing factor to give more weight to the past. A smoothing factor which treats the recent past with the distant past might be something like 0.5. The point is that it is not always necessary to automatically optimize the smoothing ...

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