I have data every 14, 15 or 16 minutes and met data every 1 minutes. I want to average met data according to the 14, 15 and 16 minutes

ACSM instrument time stamp is either 14, 15 and 16 minutes so, time 1 = a ; time 2 = b

Time ACSM

13-06-2021 00:04:38 
13-06-2021 00:20:19 
13-06-2021 00:36:01 
13-06-2021 00:51:42 
13-06-2021 01:07:23 
13-06-2021 01:23:05 
13-06-2021 01:38:46 
13-06-2021 01:54:27 
13-06-2021 02:10:08 
13-06-2021 02:25:50 
13-06-2021 02:41:31 
13-06-2021 02:57:12 
13-06-2021 03:12:54 
13-06-2021 03:28:36 
13-06-2021 03:44:18 
13-06-2021 04:00:00 
13-06-2021 04:15:42 
13-06-2021 04:31:26 

Difference between the time could be 14, 15 or 16 minutes so, x = time 1 - time 2

met data is for every 1 minute

13-06-2021 00:48  20.7  82.57  1022.52  235  3.992  0 
13-06-2021 00:49  20.58  83.42  1022.51  226.9  4.8  0 
13-06-2021 00:50  20.4  83.43  1022.49  229.5  4.323  0 
13-06-2021 00:51  20.27  83.85  1022.46  220.3  4.537  0 
13-06-2021 00:52  20.1  84.05  1022.49  225.9  6.133  0 
13-06-2021 00:53  19.87  83.95  1022.43  237.3  4.962  0 
13-06-2021 00:54  19.79  85.35  1022.48  225.1  4.812  0 
13-06-2021 00:55  19.7  86.43  1022.46  238.7  4.18  0 
13-06-2021 00:56  19.66  86.6  1022.46  233.5  5.088  0 
13-06-2021 00:57  19.62  87.82  1022.47  234.1  4.442  0 
13-06-2021 00:58  19.56  87.9  1022.44  234.6  4.045  0 
13-06-2021 00:59  19.58  87.82  1022.48  226.1  4.66  0 
13-06-2021 01:00  19.46  87.73  1022.48  234.9  5.245  0 
13-06-2021 01:01  19.4  88.67  1022.49  220.8  4.742  0 
13-06-2021 01:02  19.31  88.18  1022.48  230.9  4.515  0 
13-06-2021 01:03  19.31  88.8  1022.46  237.2  4.317  0 
13-06-2021 01:04  19.38  89.05  1022.46  221.3  3.418  0 
13-06-2021 01:05  19.43  88.6  1022.44  223.5  3.938  0 
13-06-2021 01:06  19.38  88.38  1022.42  229.4  4.088  0 
13-06-2021 01:07  19.43  89.32  1022.39  235.1  3.622  0 
13-06-2021 01:08  19.52  88.42  1022.4  223.2  3.843  0 
13-06-2021 01:09  19.49  86.78  1022.4  225.4  3.598  0 
13-06-2021 01:10  19.6  87.9  1022.38  223.8  2.555  0 
13-06-2021 01:11  19.75  86.03  1022.35  201.9  2.257  0 
13-06-2021 01:12  19.83  85.85  1022.34  224.7  3.253  0 
13-06-2021 01:13  19.96  86.68  1022.32  228  4.017  0 
13-06-2021 01:14  19.88  86.4  1022.32  217.5  3.875  0 

Time 1 from met = a

TIme 2 from met = a + x

Average the met between these two time stamp

I want to correlate these two time stamp and find the avergae of the data

[dpb]

Attach a sample file (save as .mat file and use paperclip icon)

[KSSV]

You can make use of interpolation and get the data to your desired time step. Read about interp1.

[Kavyashree N Kalkura]

i have the data , I need to avergae then according to the time step of ACSM

[dpb]

Again, you'll make it easier and more likely somebody will take some time if you attach the sample data as .mat file...

[Kavyashree N Kalkura]

Sample1 = is the time range I want

sampe2 = is the every minute data which should be averaged to the sample 1 time

Answer 1

[dpb]

load Sample1
load Sample2
ttM=table2timetable(Mettimedata);
ACSMtimedata.TimeACSM=datetime(ACSMtimedata.TimeACSM,'InputFormat',"dd-MM-uuuu HH:mm:ss");
ttMettMean=retime(ttM,ACSMtimedata.TimeACSM,'mean');

[Kavyashree N Kalkura]

Thank you very much. This worked.

For a certain portion of my data the date format is MM-dd-uuuu instead of dd-MM-uuuu , except this portion I am able to average all the data.

[dpb]

You'll have to find the portion that is formatted each way and convert them appropriately. Best would probably be to go back to the creation point and fix the inconsistency there.

There's really no programmatic way to tell for dates in which the month and day values are in the range 1:12; either field is valid -- if a value is >12, then it obviously must be a day is the best that can be done with just a numeric comparison of the values in the string. Beyond that, there has to be some other way to know "who's who in the zoo"...such as, hopefully the data are in chronological order and one can find the sections by comparison in sequence or the two formats were collected independently and later pasted together so can go back and fix the original or ...

[Kavyashree N Kalkura]

Thank you so much. I was able to change the timestamp and have successfully got the mean for all the time.