Smoothing with moving averages

When a volatile demand series is difficult to interpret visually—particularly on a logarithmic chart—a moving average offers a straightforward numerical way to expose the trend.

When to use it

Apply moving-average smoothing when historical market size rises and falls irregularly and the underlying direction is obscured.

Origins

Moving averages developed within time-series statistics as a family of filters for separating a persistent trend from short-term variation. The centred simple moving average used here averages observations on either side of the focal period. It is a descriptive smoothing technique, not the same as the moving-average error models used in more advanced forecasting.

What it is

Markets can behave like a roller-coaster: annual movement may be real, yet the individual peaks and troughs can hide the direction that matters for planning.

Where a market has no consistent year-to-year pattern, take particular care with the H stage—estimating historical growth—in the HOOF approach to demand forecasting.

A logarithmic plot with a reasoned line of best fit is one way to reduce the visual effect of volatility. A centred moving average provides a simpler non-graphical alternative by replacing each observation with the average of a window around it.