title: Google Auto ML taking huge time for forcasting
body: <p>I have around 500 time series dataset for a period of 2.5 yrs with a granularity of 1 day for each series. This amounts to roughly 1 million data points.
  I want to forecast for 2 weeks in 1 day granularity for each of the time series. There might be correlation among these 500 time series.
  After ensuring that I have data for each timestamp, we are feeding these (500) time series to autoML where each time series is identified by “series identifier”.
  So, our input to the autoML (Forecasting) is timestamp, series identifier, features, and target value. I have 30 feature which are combination of categorical and numerical.
  With this setup, if I feed to autoML, it is taking more than 20 hrs for training which is not cost effective for me.</p>
  <p>Please help me to optimized this.</p>