Working with time-series data in climate analysis

I have weather data .txt input files (about 700 files, 2 years) and each file presents a specific day info every minute. I should find the missing timesteps and corresponding data by interpolation. To be used in the desired software (EnergyPlus, TRNSYS), the data should avoid discontinuity and 1 minute timestep should be kept. To give you more clear overview you can find one week data sample in the attachment. I appreciate if you could help me.Thank you in advance.

[David Barry]

What is the desired output format? The same as the input but re-sampled on to fixed timestep?

Answer 1

[Peter Perkins]

If you have access to R2016b, this is easy with a timetable:

>> tt = timetable(datetime(2016,12,19,12,[1;3;5],0),[10;20;25])
tt =
            Time            Var1
    ____________________    ____
    19-Dec-2016 12:01:00    10  
    19-Dec-2016 12:03:00    20  
    19-Dec-2016 12:05:00    25  
>> tt = retime(tt,'minutely','spline')
tt =
            Time             Var1 
    ____________________    ______
    19-Dec-2016 12:01:00        10
    19-Dec-2016 12:02:00    15.625
    19-Dec-2016 12:03:00        20
    19-Dec-2016 12:04:00    23.125
    19-Dec-2016 12:05:00        25

Prior to R2016b, you can use interp1 with a datetime vector as the grid points and query points.

[Saeid Charani Shandiz]

Thank you Peter, fortunately I could have access to R2016b and I could solve it as you have demonstrated.