Class: timeseries
Sum of timeseries
data
ts_sm = sum(ts)
ts_sm = sum(ts,Name,Value)
returns the sum of the ts_sm
= sum(ts
)timeseries
data.
specifies
additional options with one or more ts_sm
= sum(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 sum of the
When 
Calculate the sum of each data column for a 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 sum of each data column for this timeseries object: sum(count_ts)
MATLAB^{®} returns:
768 1117 1574
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.
If the timeseries
object is uniformly sampled,
then the normalized weighting for each time is 1.0. Therefore, time
weighting has no effect.
Multiplying the data for each time by its normalized weighting.