title: Prediction Algorithms: Time Series
body: <p>Lets say that for the past few months we have been selling 1000 different products. We log the "performance" of each product (i.e. how much money it generates) every 5 minutes. A day has 288 segments of 5 minutes. Our log looks like this:</p>
  
  <pre><code>prod_1 | 2013-03-28 | 1 | 0
  prod_1 | 2013-03-28 | 2 | 9.90
  prod_1 | 2013-03-28 | 3 | 19.80
  prod_1 | 2013-03-28 | 4 | 19.80
  ...
  prod_1 | 2013-03-28 | 287 | 2326.5
  prod_1 | 2013-03-28 | 288 | 2326.5
  </code></pre>
  
  <p>So, on 28th March we sold 235 units of <code>prod_1</code> and we can draw the curve of the product's progress throughout the day. Each product/date pair is our unique object, i.e. we do not connect different days of selling the same product. We have the same data for all of the products.</p>
  
  <p>Lets say on <code>2013-03-29</code> we add a new product - <code>prod_1001</code>. The last line in our log for this product reads:</p>
  
  <pre><code>prod_1001 | 2013-03-29 | 153 | 804,6
  </code></pre>
  
  <p><strong>Question:</strong> what machine algorithm should we use to predict the revenue that this specific product will have generated at the end of the day?</p>
  
  <pre><code>prod_1001 | 2013-03-29 | 288 | ???
  </code></pre>