title: Using statsmodels.seasonal_decompose() without DatetimeIndex but with Known Frequency
body: <p>I have a time-series signal I would like to decompose in Python, so I turned to statsmodels.seasonal_decompose(). My data has frequency of 48 (half-hourly). I was getting the same error as <a href="https://stackoverflow.com/questions/34457281/decomposing-trend-seasonal-and-residual-time-series-elements">this questioner</a>, where the solution was to change from an Int index to a DatetimeIndex. But I don't know the actual dates/times my data is from.</p>
  
  <p>In <a href="https://github.com/statsmodels/statsmodels/issues/3225" rel="noreferrer">this github thread</a>, one of the statsmodels contributors says that </p>
  
  <blockquote>
    <p>"In 0.8, you should be able to specify freq as keyword argument to
    override the index."</p>
  </blockquote>
  
  <p>But this seems not to be the case for me. Here is a minimal code example illustrating my issue:</p>
  
  <pre><code>import statsmodels.api as sm
  dta = pd.Series([x%3 for x in range(100)])
  decomposed = sm.tsa.seasonal_decompose(dta, freq=3)
  
  AttributeError: 'RangeIndex' object has no attribute 'inferred_freq'
  </code></pre>
  
  <p>Version info:</p>
  
  <pre><code>import statsmodels
  print(statsmodels.__version__)
  0.8.0
  </code></pre>
  
  <p>Is there a way to decompose a time-series in statsmodels with a specified frequency but without a DatetimeIndex?</p>
  
  <p>If not, is there a preferred alternative for doing this in Python? I checked out the Seasonal package, but <a href="https://github.com/welch/seasonal" rel="noreferrer">its github</a> lists 0 downloads/month, one contributor, and last commit 9 months ago, so I'm not sure I want to rely on that for my project.</p>