# ma

0th

Percentile

##### Moving-average smoothing

ma computes a simple moving average smoother of a given time series.

Keywords
ts
##### Usage
ma(x, order, centre=TRUE)
##### Arguments
x
Univariate time series
order
Order of moving average smoother
centre
If TRUE, then the moving average is centred for even orders.
##### Details

The moving average smoother averages the nearest order periods of each observation. As neighbouring observations of a time series are likely to be similar in value, averaging eliminates some of the randomness in the data, leaving a smooth trend-cycle component. $$\hat{T}_{t} = \frac{1}{m} \sum_{j=-k}^k y_{t+j}$$ where $k=(m-1)/2$

When an even order is specified, the observations averaged will include one more observation from the future than the past (k is rounded up). If centre is TRUE, the value from two moving averages (where k is rounded up and down respectively) are averaged, centering the moving average.

##### Value

decompose

• ma
##### Examples
plot(wineind)
sm <- ma(wineind,order=12)
lines(sm,col="red")

Documentation reproduced from package forecast, version 7.3, License: GPL (>= 2)

### Community examples

twigt.arie@gmail.com at Sep 23, 2018 forecast v8.4

## Example to get an understanding how values are calculated by applying the ma() function. {r} # create a numerical vector which is easy to understand numbers <- c(3, 5, 3, 6, 4, 5, 3, 4, 4, 6, 3, 5, 4, 6, 3, 5, 4, 4, 6, 3, 5) # apply the 'ma' function ma(numbers, order = 5)  Calculate the average for the first 5 numbers by hand. {r} (sum(3, 5, 3, 6, 4))/5