title: How to pass spatio-temporal data into sklearn models?
body: <p>I am new to machine learning and trying to master my first steps with scikit-learn. I would like to calculate an interpolation, based on spatio-temporal sensor data. I have a larger number of measuring stations that all measure data at the same time (hourly). For each measuring station I have a unique coordinate (X, Y, Z). My measured/fixed values for each measuring station thus consist of:</p>
  
  <pre><code>     Timestamp,          X,           Y,        Z,   Value + Possible further values
  2018-05-04 00:00:00, 32362422.00, 5656123.00, 54.28, 4.28, ..
  2018-05-04 00:00:00, 32365418.00, 5656413.00, 72.47, 3.12, ..
  2018-05-04 00:00:00, 32360290.00, 5656973.00, 51.11, 2.50, ..
  ...
  2018-05-04 01:00:00, 32362422.00, 5656123.00, 54.28, 4.53, ..
  2018-05-04 01:00:00, 32365418.00, 5656413.00, 72.47, 3.27, ..
  ...
  
  (train data)
  </code></pre>
  
  <p>(All data is available as a CSV file).</p>
  
  <p>I want to interpolate values for coordinates between my measuring stations.
  Of course, the values to be interpolated should depend on new values not yet known to the model</p>
  
  <pre><code>Sensor Data again
  2018-05-22 16:00:00, 32362422.00, 5656123.00, 54.28, 0.29, ..
  2018-05-22 16:00:00, 32365418.00, 5656413.00, 72.47, 1.12, ..
  2018-05-22 16:00:00, 32360290.00, 5656973.00, 51.11, 0.73, ..
  ... -&gt; All Measurements
  
  New data to be interpolated in a grid:
  2018-05-22 16:00:00, 32362500.00, 5656150.00, 55.81, ?, ..
  2018-05-22 16:00:00, 32362500.00, 5656200.00, 56.44, ?, ..
  ...
  (interpolation data)
  </code></pre>
  
  <p>For the calculation I would like to use the Random Forest Regressor.</p>
  
  <p>However, I'm a bit overwhelmed:</p>
  
  <ol>
  <li><p>How can I pass my data as summarized blocks (with identical timestamp) to my model?</p></li>
  <li><p>How do I best validate such records? Also for the cross-validation my data must be bundled?</p></li>
  </ol>
  
  <p>I am very grateful for any advice. The answers may also be somewhat detailed.
  Cheers.</p>
  
  <p>EDIT:</p>
  
  <ol>
  <li><p>Of course, my labels are the values measured by the stations. </p></li>
  <li><p>I tried a One-Hot-Encoding for each timestamp (equal time = same group). In next step want to predict a value for each coordinate of my digital elevation model.</p></li>
  <li><p>Not yet, i thought RF would create the simpliest/best model. I will try it at next.</p></li>
  </ol>