title: How can you do time series forecasting in Tensorflow (or with other tools) where features of the label timestep are known?
body: <p>This is a question about a general approach rather than a specific coding problem. I'm trying to do time series forecasting with Tensorflow where features of the label timestep are known to the model.  E.g. a human trying to predict a variable a week from now would know things that are going to happen in the next week that will affect that variable. So a window of 20 timesteps where the label is the 20th timestep would look something like this:</p>
  <ul>
  <li>Timesteps 1-19 would each have a set of features plus the timeseries data</li>
  <li>Timestep 20 would have a set of features which are known, plus the timeseries label which is unknown</li>
  </ul>
  <p>Is there a model that could handle this sort of data? I've gone through the Tensorflow <a href="https://www.tensorflow.org/tutorials/structured_data/time_series#linear_model" rel="nofollow noreferrer">time series forecasting tutorial</a>, done a Coursera course on Tensorflow time series forecasting and searched elsewhere but I can't find anything. I'm fairly new to this so apologies for any imprecise language.</p>