title: The most proper model for combining both input types(TIME_SERIES_INPUT and AUXILIARY(static)_INPUTS)?
body: <p>So I have searched a few posts and Issue posts about the reshaping problem but none of these solutions seems to work for me so far.</p>
  
  <p>The goal of the project: predicting the week-based sales by each store and SKU(the properties of clothes: e.g. colour/size).</p>
  
  <p>The structure of the dataset is like following:</p>
  
  <p><code>Week, store_id, color, size, last_week_sales, last_2week_sales_(sales two weeks),actual_sales(the one we want to predict)
  1, 341, red, LL, 0, 1, 1
  1, 341, yellow, M, 2, 4, 2
  1, 341, blue, S, 2, 2, 3
  2, 342, blue, M, 2, 3, 1
  2, 342, green, S, 2, 3, 2</code></p>
  
  <p>So for each week, every record is unique by the combination of the features(properties of the clothes and the store_id etc.)</p>
  
  <p>The number of records for each week is not the same.</p>
  
  <p><strong>Update on 8-23-2018:</strong></p>
  
  <p>I tried the fully-connected NN, yet the correctness is about 75% which can't be improved in various ways. I wondered whether there's another way around dealing this issue, thanks in advance!</p>