title: Naive forecast day-ahead
body: <p>I want to do a naive forecast with a dataset I have and I am struggling a bit doing so.</p>
  
  <pre><code>    values = DataFrame(dataset.iloc[:, -1])
      Y_naive = pd.concat([values.shift(24), values], axis=1)
      Y_naive.columns = ['t', 't+1']
      x = Y_naive.values
  </code></pre>
  
  <p>What I basically have is hourly data where I want to compare the last column regarding its manifestation {0,1}. Since I want to compare this naive forecast with other day-ahead predictors I want to use the data of one day before (shift(24)) to predict the actual manifestation. 
  Naive forecast:</p>
  
  <pre><code>    def naive_forecast(x):
          return x
      predictions = list()
      for x in test_x:
          yhat = naive_forecast(x)
          predictions.append(yhat)
  </code></pre>
  
  <p>For me it is very unclear how to do the mapping process. That means how to give over the argument that for binary classification testing I want to use the data from 24 hours before to map it on the data now for the entire dataset iteratively. (<a href="https://en.wikipedia.org/wiki/Forecasting#Na.C3.AFve_approach" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Forecasting#Na.C3.AFve_approach</a>)</p>