interpolate data of a dataset of precipitation for the past 10 years to a 5-minute time series (instead of the original 15-minute sampling interval) in matlab

You can use the resample function in MATLAB to interpolate the data. Here's an example code snippet:

main.m
% Assume you have a dataset of precipitation with a sampling interval of
% 15 minutes
load('precipitation_data.mat'); % Replace with your own filename

% Define the original time vector
t_orig = datetime('today')-calyears(10):minutes(15):datetime('today');

% Define the new time vector with a sampling interval of 5 minutes
t_new = datetime('today')-calyears(10):minutes(5):datetime('today');

% Resample the precipitation data to the new time vector using linear interpolation
precip_new = resample(precipitation, t_new, 'linear');

% Plot the original and resampled data
plot(t_orig, precipitation, 'o-', t_new, precip_new, '+-')
legend('Original', 'Resampled')
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In this example, precipitation_data.mat is assumed to contain a variable precipitation with the precipitation data. You should replace this with your own dataset.

The code first defines the original time vector t_orig using the datetime function. It then defines the new time vector t_new with a sampling interval of 5 minutes.

Finally, the resample function is used to interpolate the precipitation data to the new time vector using linear interpolation. The resulting interpolated data is stored in the variable precip_new. The code then plots the original and resampled data to visualize the interpolation result.

Note that there are other interpolation methods available besides linear interpolation, such as cubic or spline interpolation. You can specify the interpolation method as the third argument to the resample function.