title: Impute time series data in python using given set of features
body: <p>So my data looks like:</p>
  
  <pre><code>year, y, x1, x2, x3, x4
  2009, 0.5, 0.4, 0.4, 0.9
  2013, nan, 0.4, 0.5, 0.8
  2020, 0.8, 0.39, 0.51, 0.7
  </code></pre>
  
  <p>The data is year-wise but the interval between each year is not consistent. Value of y depends both on time and the features. But in some cases y is missing which I need the most. Other features can be missing too but mostly they are all there. I have tried imputing data through <code>df.interpolate()</code> function but values does not fit well in the interval for most of the functions. I have tried ARIMA, LSTM and others but they do not consider input features. I have considered using regression techniques too but they do not incorporate time series nature of the data.</p>
  
  <p>So what is the best approach for this case. i.e.</p>
  
  <p><strong>How to impute Time Series values based on input features?</strong></p>