Moving-average smoothing

Naïve methods


Why:

When they’re too many noises, sometimes moving-average
smoothing can help to identity trend.

Apply:

Choose a window size , estimate the moving average at time be the average value of the observations around time

Setup:

Default setting: rolling backward with window size k:

example : k = 3

when center = True, k is odd and k = 2q+1

when center = True, k is even, k = 2q

Choose window size :

  1. the window size of the filter should be chosen based on the application and the data being filtered. for example, weekly, monthly,quarterly
  2. It’s common to choose the seasonality period if exists
  3. The bigger the window, the smoother the trend

Key words:


TAGS

#timeseries