The Exponentially Weighted Moving Average
Applied To The Control And Monitoring Of
Varying Sample Sizes
Author(s)
J. E. Everett
Abstract
The exponentially weighted moving average (EWMA) can be used to report the
smoothed history of a production process, and has some considerable advantages
over a simple moving average (MA). Discussion of these advantages includes
comparison of the filter characteristics of the EWMA and MA in the frequency
domain. It is shown that the EWMA provides a much smoother filter than does
the MA, and the corresponding implications of this difference are examined in
the time domain. In smoothing a production process, the successive entities
being smoothed commonly have varying \“weights”, where the weights may be
such quantities as tonnage, value or time interval. Standard textbook treatments
of moving averages and exponential smoothing are generally confined to equal
spaced data of equal weight. Adapting the average to cope with items of varying
weight is shown to be trivial for the case of MA, but is not so obvious for the
EWMA. This paper shows how the exponential smoothing constant has to be
adapted to provide a consistent EWMA. Applications of the EWMA in process
control are discussed, with particular reference to quality control in the mining
industry.
Keywords: quality control, forecasting, exponential smoothing, sample size.
1 Introduction
It is common to consider a series of observations, xn, where each observation is
equivalently spaced in time or distance or some other relevant dimension.
For forecasting and for system control purposes, it is useful to have some
summary of the performance up to the nth observation.
Keywords
quality control, forecasting, exponential smoothing, sample size
Related Book
Other papers in this volume
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