title: Time Series Forecasting in Python for Numerous Time Series
body: <p>I have a network of sensors that output hourly/daily reads.
  Some of these sensors are faulty and send out reads sporadically (once a week, once a month).  The sensors are not connected to one another, but they can be classified.  I ran a clustering algorithm to classify them based on their dry parameters (each is located in a different location with slightly varying definitions and specs).</p>
  
  <p>For each faulty sensor, I predict/check the cluster to which it belongs and now I want to predict its read based on the reads of the other - non-faulty - sensors in the cluster.</p>
  
  <p>It seems that ARIMA is the best fit for the data, but is there a way to build an ARIMA model for many time series and applying it to the faulty sensor's data?  Is there a better algorithm suited for this problem?</p>
  
  <p>All help is appreciated.</p>