title: Normalize Time Series - Scikit
body: <p>I have:</p>
  <ol>
  <li>3 wikipedia article access counts (weekly)  (A-B-C)</li>
  <li>Ground truth data (weekly)</li>
  <li>Total wikipedia english article traffic counts (weekly)</li>
  </ol>
  <p>My purpose is, build a multiple linear regression with <strong>3 wikipedia article access counts</strong> and try to predict future <strong>ground truth data</strong>.</p>
  <p>Before start to build multiple linear regression, I want to make some pre processing( normalization or scaling ) on my 3 wikipedia access count data.</p>
  <p>My data format is like this.</p>
  <pre><code>    date     | A (x1)     | B (x2)  |  C (x3) | total_en     | ground truth(y)
  
   01/01/2008  |   5611     |   606   |    376  |  1467923911  | 3.13599886
   08/01/2008  |   8147     |   912   |    569  |  1627405409  | 2.53335614
   15/01/2008  |   9809     |   873   |    597  |  1744099880  | 2.91287713
   22/01/2008  |   12020    |   882   |    600  |  1804646235  | 3.44497102  
   ...         |    ...     |   ...   |    ...  |    ...       | ...
  </code></pre>
  <p>Without normalization I build my multiple linear regression like this.</p>
  <blockquote>
  <p>wiki3.shape = (150,3) // include A-B-C article with numpy array</p>
  <p>ground_truth = (150,1) // include ground truth data in numpy array</p>
  </blockquote>
  <pre><code>X_train, X_test, y_train, y_test = cross_validation.train_test_split(wiki3, ground_truth, test_size=0.3, random_state=1)
  
  model = linear_model.LinearRegression()
  model.fit(X_train, y_train)
  
  predictions = model.predict(X_test)
  </code></pre>
  <p><strong>My question is for better results How can I normalize/scale my x1,x2,x3 and y data ?</strong></p>
  <p><strong>Should I normalize each article with the total english article traffic or should I use another way ?</strong></p>
  <p><strong>Is K-Fold cross validation sensible for time-series ?</strong></p>
  <p>Thanks.</p>