movavg: Moving Average Filters In pracma: Practical Numerical Math Functions

Description

Different types of moving average of a time series.

Usage

 1 movavg(x, n, type=c("s", "t", "w", "m", "e", "r"))

Arguments

 x time series as numeric vector. n backward window length. type one of 's', 't', 'w', 'm', 'e', or 'r'; default is 's'.

Details

Types of available moving averages are:

• s for “simple”, it computes the simple moving average. n indicates the number of previous data points used with the current data point when calculating the moving average.

• t for “triangular”, it computes the triangular moving average by calculating the first simple moving average with window width of ceil(n+1)/2; then it calculates a second simple moving average on the first moving average with the same window size.

• w for “weighted", it calculates the weighted moving average by supplying weights for each element in the moving window. Here the reduction of weights follows a linear trend.

• m for “modified", it calculates the modified moving average. The first modified moving average is calculated like a simple moving average. Subsequent values are calculated by adding the new value and subtracting the last average from the resulting sum.

• e for“exponential", it computes the exponentially weighted moving average. The exponential moving average is a weighted moving average that reduces influences by applying more weight to recent data points () reduction factor 2/(n+1); or

• r for“running", this is an exponential moving average with a reduction factor of 1/n [same as the modified average?].

Value

Vector the same length as time series x.

References

Matlab Techdoc 