title: Linear regression with pandas time series
body: <p>I have a dataframe object which contains 1 seconds intervals of the EUR_USD currency pair. But in theory it could be any interval and in this case it could look like this:</p>
  
  <pre><code>2015-11-10 01:00:00+01:00    1.07616
  2015-11-10 01:01:00+01:00    1.07605
  2015-11-10 01:02:00+01:00    1.07590
  2015-11-10 01:03:00+01:00    1.07592
  2015-11-10 01:04:00+01:00    1.07583
  </code></pre>
  
  <p>I'd like to use linear regression to draw a trend line from the data in dataframe, but I'm not sure what the best way are to do that with time series, and even such a small interval of time series.</p>
  
  <p>So far I've messed around by replacing the time by (and this is just to show where I'd like to go with it) a list ranging from 0 to the time series list length.</p>
  
  <pre><code>x = list(range(0, len(df.index.tolist()), 1))
  y = df["closeAsk"].tolist()
  </code></pre>
  
  <p>Using numpy to do the math magic</p>
  
  <pre><code>fit = np.polyfit(x,y,1)
  fit_fn = np.poly1d(fit)
  </code></pre>
  
  <p>Lastly I draw the function along with the df["closeAsk"] to make sense of the trend.</p>
  
  <pre><code>plt.plot(x,df["closeAsk"], '-')
  plt.plot(x,y, 'yo', x, fit_fn(x), '--k')
  plt.show()
  </code></pre>
  
  <p>However now the x-axis is just meaningless numbers, instead I'd like for them to show the time series.</p>