Use of time Series

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Cedric Mariage
Cedric Mariage on 13 Aug 2015
Answered: Peter Perkins on 18 Aug 2015
I'm looking to do some regression between two or more time series . I'm wondering if i should use the variable in matlab "timeseries". I don't have a point in the same time for each time series . I fixed this issue by creating an uniform time vector. Then , for each of my time series , I create a new time series with the same data but the uniform vector . when I don't have any data for one time - I take the closest data. This is working find but it's slow. Is there any better solution ? I put below the slow code which create my fulltime series Thanks In advance
% Create an uniforme time series - with a %t of Nsec.
%time and data represent my non uniform time series
function [fulltime,sol] = CreationFullTimeSeries(time,data,Nsec,start_date,end_date,start_time,end_time)
t1= datetime(strcat(start_date,start_time),'InputFormat','yyyyMMddHHmmSS');
t2= datetime(strcat(end_date,end_time),'InputFormat','yyyyMMddHHmmSS');
fulltime = transpose(t1:seconds(Nsec):t2);
sol = zeros(length(fulltime),size(data,2));
for i=1:length(fulltime)
while and(datetime(time(j,:))<fulltime(i,1) ,j<size(time,1))

Answers (2)

Brian Neiswander
Brian Neiswander on 17 Aug 2015
Using a simple linear interpolation should be the easiest method to accomplish this. Let's say you have some sample measurement data in vectors named "time" and "data":
%define the starting and ending times of your measurements
start_date = '20100101'; %sstring containing tarting date in yyyyMMdd
end_date = '20100102'; %string containing ending date in yyyyMMdd
start_time = '120000'; %string containing starting time in HHmmSS
end_time = '120000'; %string containing ending time in HHmmSS
%make a time vector for the measurement data
Nsample = 2000;
t1 = datetime(strcat(start_date, start_time), 'InputFormat', 'yyyyMMddHHmmSS');
t2 = datetime(strcat(end_date, end_time), 'InputFormat', 'yyyyMMddHHmmSS');
time = t1:seconds(Nsample):t2;
%make some sample measurement data using a sine wave
data = sin(2*pi*linspace(0,1,length(time)));
Now, let's create a regularly spaced time vector "timeInterp" that you want to interpolate the measured data onto:
%make interpolating time vector
Nsec = 750;
timeInterp = t1:seconds(Nsec):t2;
Note that both the "time" and "timeInterp" vectors contain date strings. These will need to be converted to numerical values before interpolating. This can be done using the "datenum" function:
%convert date string vectors to date numbers
timeNum = datenum(time);
timeInterpNum = datenum(timeInterp);
Now you can use the "fit" function to interpolate the data onto the regularly spaced time vector:
%make a fit object that will linearly inerpolate over the data set
myfit = fit(timeNum(:), data(:), 'linearinterp');
%interpolate the data onto the regularly spaced time vector using the fit object
dataInterp = myfit(timeInterpNum);
Note that the "fit" function is a part of the Curve Fitting Toolbox. You can check the interpolated data against the original data in a plot:
%plot the data
plot(timeNum, data, 'x', 'markersize',10);
hold on;
plot(timeInterpNum, dataInterp, 'k.');
datetick('x'); %change the x-axis labels to date strings
legend({'Measured data','Interpolated data'});
This approach will be much faster than using "for" and "while" loops. For more information, check the following documentation pages:

Peter Perkins
Peter Perkins on 18 Aug 2015
Cedric, while the timeseries class does not support datetime arrays, it does have methods to synchronize or resample time series to a common time vector. You might find it convenient to use timeseries, even if you subsequently pull the time and data back out.


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