# Updating window weight accommodating large people on airline

One disadvantage of this technique is that it cannot be used on the first k −1 terms of the time series without the addition of values created by some other means.

This means effectively extrapolating outside the existing data, and the validity of this section would therefore be questionable and not a direct representation of the data.

This simple form of exponential smoothing is also known as an exponentially weighted moving average (EWMA).

Technically it can also be classified as an autoregressive integrated moving average (ARIMA) (0,1,1) model with no constant term..

The term smoothing factor applied to α here is something of a misnomer, as larger values of α actually reduce the level of smoothing, and in the limiting case with α = 1 the output series is just the current observation.

Alternatively, a statistical technique may be used to optimize the value of α.

For example, the method of least squares might be used to determine the value of α for which the sum of the quantities Unlike some other smoothing methods, such as the simple moving average, this technique does not require any minimum number of observations to be made before it begins to produce results.

In practice, however, a “good average” will not be achieved until several samples have been averaged together; for example, a constant signal will take approximately 3/α stages to reach 95% of the actual value.

To accurately reconstruct the original signal without information loss all stages of the exponential moving average must also be available, because older samples decay in weight exponentially.