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ts_var = var(ts)
ts_var = var(ts,'PropertyName1',PropertyValue1,...)
ts_var = var(ts) returns the variance of ts.data. When ts.Data is a vector, ts_var is the variance of ts.Data values. When ts.Data is a matrix, ts_var is a row vector containing the variance of each column of ts.Data (when IsTimeFirst is true and the first dimension of ts is aligned with time). For the N-dimensional ts.Data array, var always operates along the first nonsingleton dimension of ts.Data.
ts_var = var(ts,'PropertyName1',PropertyValue1,...) specifies the following optional input arguments:
'MissingData' property has two possible values, 'remove' (default) or 'interpolate', indicating how to treat missing data during the calculation.
'Quality' values are specified by an integer vector, indicating which quality codes represent missing samples (for vector data) or missing observations (for data arrays with two or more dimensions).
'Weighting' property has two possible values, 'none' (default)
or 'time'.
When you specify 'time',
larger time values correspond to larger weights.
The following example shows how to calculate the variance values of a multi-variate timeseries object.
load count.dat
Create a timeseries object with 24 time values.
count_ts = timeseries(count,[1:24],'Name','CountPerSecond')
Calculate the variance of each data column for this timeseries object.
var(count_ts)
ans =
1.0e+003 *
0.6437 1.7144 4.6278
The variance is calculated independently for each data column in the timeseries object.
iqr (timeseries), mean (timeseries), median (timeseries), std (timeseries), timeseries
![]() | var | varargin | ![]() |

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