This requires reading it as a table, converting it to a timetable and then use the retime function —
T1 = readtable('https://www.mathworks.com/matlabcentral/answers/uploaded_files/623923/Observation%20Data.csv', 'VariableNamingRule','preserve');
TT1 = table2timetable(T1); % Copy & Convert To 'timetable'
TT1 = retime(TT1, 'hourly','fillwithmissing'); % Interpolate If Necessary & Require Hourly Time Step
TT1 = retime(TT1,'hourly',@(x)movmean(x,30*24)); % Use 'movmean'
figure
subplot(2,1,1)
plot(T1.Date, T1.Obs)
grid
xlabel('Time')
ylabel('Obs')
title('Original Table')
subplot(2,1,2)
plot(TT1.Date, TT1.Obs)
grid
xlabel('Time')
ylabel('Obs')
title('Timetable With ''movmean''')
I do not see much difference in the plots or numerical output, however it does not throw any errors, so I assume it is working correctly.
EDIT — (20 May 2021 at 14:06)
This is likely closer to what you want —
TT12 = movmean(TT1.Obs,30*24,'omitnan', 'Endpoints','shrink'); % Use 'movmean'
figure
subplot(2,1,1)
plot(T1.Date, T1.Obs)
grid
xlabel('Time')
ylabel('Obs')
title('Original Table')
subplot(2,1,2)
plot(TT1.Date, TT12)
grid
xlabel('Time')
ylabel('Obs')
title('Timetable With ''movmean''')
The ‘spikes’ are artifacts due to the discontinuities. Eliminating then would likely not be easy, and would likely involve simply detecting them using islocalmax and islocalmin, and then simply deleting them.
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