ts_std = std(ts)
ts_std = std(ts,Name,Value)
returns the standard deviation of the ts_std
= std(ts
)timeseries
data.
specifies
additional options specified with one or more ts_std
= std(ts
,Name,Value
)Name,Value
pair
arguments.

The 
Specify optional commaseparated pairs of Name,Value
arguments.
Name
is the argument
name and Value
is the corresponding
value. Name
must appear
inside single quotes (' '
).
You can specify several name and value pair
arguments in any order as Name1,Value1,...,NameN,ValueN
.

A string specifying one of two possible values, Default: 

A vector of integers, indicating which quality codes represent missing samples (for vector data) or missing observations (for data arrays with two or more dimensions). 

A string specifying one of two possible values, 

The standard deviation of
When 
The following example finds the standard deviation for a timeseries
object. MATLAB^{®} calculates
the standard deviation for each data column in the timeseries
object.
% Load a 24by3 data array: load count.dat % Create a timeseries object with 24 time values: count_ts = timeseries(count,1:24,'Name','CountPerSecond'); % Calculate the standard deviation of each data column for this % timeseries object: std(count_ts)
MATLAB returns:
25.3703 41.4057 68.0281
MATLAB determines weighting by:
Attaching a weighting to each time value, depending on its order, as follows:
First time point — The duration of the first
time interval (t(2)  t(1))
.
Time point that is neither the first nor last time
point — The duration between the midpoint of the previous time
interval to the midpoint of the subsequent time interval ((t(k
+ 1)  t(k))/2 + (t(k)  t(k  1))/2)
.
Last time point — The duration of the last
time interval (t(end)  t(end  1))
.
Normalizing the weighting for each time by dividing each weighting by the mean of all weightings.
Note:
If the 
Multiplying the data for each time by its normalized weighting.