title: Why NUMPY correlate and corrcoef return different values and how to "normalize" a correlate in "full" mode?
body: <p>I'm trying to use some Time Series Analysis in Python, using Numpy.</p>
  
  <p>I have two somewhat medium-sized series, with 20k values each and I want to check the sliding correlation.</p>
  
  <p>The corrcoef gives me as output a Matrix of auto-correlation/correlation coefficients. Nothing useful by itself in my case, as one of the series contains a lag.</p>
  
  <p>The correlate function (in mode="full") returns a 40k elements list that DO look like the kind of result I'm aiming for (the peak value is as far from the center of the list as the Lag would indicate), but the values are all weird - up to 500, when I was expecting something from -1 to 1.</p>
  
  <p>I can't just divide it all by the max value; I know the max correlation isn't 1.</p>
  
  <p>How could I normalize the "cross-correlation" (correlation in "full" mode) so the return values would be the correlation on each lag step instead those very large, strange values?</p>