Movavg function doesn't compute values for the entire timeseries

I use the movavg function twice to do a double exponential smoothing on timeseries data, and for some reasong it's only computing it for the first few points in each column. below is the code I use and I attached the timetable.

load august_filtered_data.mat
figure
plot(signal_timetable.Time,signal_timetable.Signal)

smoothed_data = twoexp_movavg(signal_timetable,60);
figure
plot(smoothed_data.Time,smoothed_data.Signal)

function [TT] = twoexp_movavg(TT,numpts)

ma = movavg(TT{:,:},'exponential',numpts);
dma = 2*ma - movavg(ma,'exponential',numpts);
TT{:,:} = dma;

end

[Poison Idea fan]

I'm sorry I should've been more diligent. By mistake, i uploaded the output of the function I included. I also changed the window size to 1 to see if I would get the same number of points output as a window size of 60 points. Below is a more accurate example of how I call the function. This is timetable is about an hours worth of data out of the weeks of data in the timeseries.

figure

load august_filtered_data.mat
figure
plot(signal_timetable.Time,signal_timetable.Signal)

smoothed_data = twoexp_movavg(signal_timetable,60);

figure
plot(smoothed_data.Time,smoothed_data.Signal)

Answer 1

[Star Strider]

Use the fillmissing function —

LD = load(websave('august_filtered_data','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1120940/august_filtered_data.mat'))

LD = struct with fields:
    signal_timetable: [5000×4 timetable]

signal_timetable = LD.signal_timetable

signal_timetable = 5000×4 timetable
           Time                           Signal                                  power                    filter state              Tau           
    ___________________    ____________________________________    ____________________________________    ____________    ________________________

    2022-08-12 12:05:45    11.589    4.8807    3.3609    4.0757     563.4    596.26    463.61    697.23         1          7.1129e-05    2.7593e-05
    2022-08-12 12:05:46    12.201    4.7314    2.9541    4.1898     564.9    595.89    463.76    699.01         1          7.1218e-05    2.7614e-05
    2022-08-12 12:05:47    12.387       NaN       NaN     4.093    563.55    596.64     467.3     698.6         1          7.1079e-05     2.756e-05
    2022-08-12 12:05:48    11.609    5.6979    2.2949    3.8749    562.94    598.08    464.41    698.33         1          7.1172e-05      2.76e-05
    2022-08-12 12:05:49    11.483    4.9726       NaN    3.8147    563.29    596.58    466.04    698.45         1          7.1256e-05    2.7607e-05
    2022-08-12 12:05:50    11.232    4.9261       NaN    3.2259    563.14    600.08    467.07     699.1         1          7.1342e-05    2.7585e-05
    2022-08-12 12:05:51    11.323    5.6548    2.6544    3.4456    562.36     604.4    465.48    698.18         1          7.1049e-05    2.7515e-05
    2022-08-12 12:05:52    11.399    5.4601    3.0548    3.8976    561.74    607.38    465.12     698.1         1          7.1229e-05    2.7558e-05
    2022-08-12 12:05:53    11.097    5.2122       NaN    2.9902    562.23    605.41    466.19    699.12         1          7.1339e-05    2.7567e-05
    2022-08-12 12:05:54    11.916    5.4602    3.4976    3.7028    562.71    602.71    465.85    698.96         1          7.1223e-05    2.7573e-05
    2022-08-12 12:05:55    11.993       NaN    2.8391    3.2377    562.36    602.79     464.4    698.01         1          7.1142e-05    2.7562e-05
    2022-08-12 12:05:56    12.065     3.916    3.5892       NaN    563.01     609.2    462.94    698.79         1          7.1209e-05    2.7597e-05
    2022-08-12 12:05:57    11.284    4.8572    3.3282       NaN    562.83     606.7    463.16     697.8         1          7.1171e-05    2.7542e-05
    2022-08-12 12:05:58    12.256    5.4621     3.124    3.7976    564.55    608.55    462.84    699.32         1          7.1159e-05    2.7577e-05
    2022-08-12 12:05:59     12.37    4.5209    2.6034    3.1908    564.21    608.76    460.09    699.66         1           7.127e-05    2.7541e-05
    2022-08-12 12:06:00    11.756    4.4921    3.5136       NaN    563.71    608.37     458.6    698.06         1          7.1124e-05    2.7549e-05

NrNaNs = nnz(isnan(signal_timetable.Signal))

NrNaNs = 5385

NrNaNs4 = nnz(isnan(signal_timetable.Signal(:,4)))

NrNaNs4 = 1458

NotNan4 = numel(signal_timetable.Signal(:,4)) - NrNaNs4

NotNan4 = 3542

signal_timetable = fillmissing(signal_timetable, 'linear')

signal_timetable = 5000×4 timetable
           Time                           Signal                                  power                    filter state              Tau           
    ___________________    ____________________________________    ____________________________________    ____________    ________________________

    2022-08-12 12:05:45    11.589    4.8807    3.3609    4.0757     563.4    596.26    463.61    697.23         1          7.1129e-05    2.7593e-05
    2022-08-12 12:05:46    12.201    4.7314    2.9541    4.1898     564.9    595.89    463.76    699.01         1          7.1218e-05    2.7614e-05
    2022-08-12 12:05:47    12.387    5.2146    2.6245     4.093    563.55    596.64     467.3     698.6         1          7.1079e-05     2.756e-05
    2022-08-12 12:05:48    11.609    5.6979    2.2949    3.8749    562.94    598.08    464.41    698.33         1          7.1172e-05      2.76e-05
    2022-08-12 12:05:49    11.483    4.9726    2.4147    3.8147    563.29    596.58    466.04    698.45         1          7.1256e-05    2.7607e-05
    2022-08-12 12:05:50    11.232    4.9261    2.5346    3.2259    563.14    600.08    467.07     699.1         1          7.1342e-05    2.7585e-05
    2022-08-12 12:05:51    11.323    5.6548    2.6544    3.4456    562.36     604.4    465.48    698.18         1          7.1049e-05    2.7515e-05
    2022-08-12 12:05:52    11.399    5.4601    3.0548    3.8976    561.74    607.38    465.12     698.1         1          7.1229e-05    2.7558e-05
    2022-08-12 12:05:53    11.097    5.2122    3.2762    2.9902    562.23    605.41    466.19    699.12         1          7.1339e-05    2.7567e-05
    2022-08-12 12:05:54    11.916    5.4602    3.4976    3.7028    562.71    602.71    465.85    698.96         1          7.1223e-05    2.7573e-05
    2022-08-12 12:05:55    11.993    4.6881    2.8391    3.2377    562.36    602.79     464.4    698.01         1          7.1142e-05    2.7562e-05
    2022-08-12 12:05:56    12.065     3.916    3.5892    3.4243    563.01     609.2    462.94    698.79         1          7.1209e-05    2.7597e-05
    2022-08-12 12:05:57    11.284    4.8572    3.3282     3.611    562.83     606.7    463.16     697.8         1          7.1171e-05    2.7542e-05
    2022-08-12 12:05:58    12.256    5.4621     3.124    3.7976    564.55    608.55    462.84    699.32         1          7.1159e-05    2.7577e-05
    2022-08-12 12:05:59     12.37    4.5209    2.6034    3.1908    564.21    608.76    460.09    699.66         1           7.127e-05    2.7541e-05
    2022-08-12 12:06:00    11.756    4.4921    3.5136    3.1423    563.71    608.37     458.6    698.06         1          7.1124e-05    2.7549e-05

figure
plot(signal_timetable.Time, signal_timetable.Signal, '-')
grid
xlabel('Time')
ylabel('Amplitude')
title('Original')
legend(compose('Signal %d',1:size(signal_timetable.Signal,2)), 'Location','best')

signal_timetablef = twoexp_movavg(signal_timetable,60);

figure
plot(signal_timetablef.Time, signal_timetablef.Signal,'-')
grid
xlabel('Time')
ylabel('Amplitude')
title('Filtered')
legend(compose('Signal %d',1:size(signal_timetable.Signal,2)), 'Location','best')

function [TT] = twoexp_movavg(TT,numpts)

ma = movavg(TT{:,:},'exponential',numpts);
dma = 2*ma - movavg(ma,'exponential',numpts);
TT{:,:} = dma;

end

Experiment with different function parameters or different fillmissing 'method' options to get the desired result.

Answer 2

[Poison Idea fan]

The issue is coming from NaN values in the timetable. I treat the data before I smooth it with a filter for erroneous points and replace them with NaN so I still have 1 second time resolution. If I remove them before calling the smoothing function the issue goes away. I don't think movavg has an option to omit NaN values like the mean function does.

[Poison Idea fan]

I accepted before the edit. I went to change it to accept yours but alas, it has been deleted. All my fault though.