title: Forecasting in R using Holt's Linear model
body: <p>I am trying to forecast data with a downward trend. I understand that Holt's linear model might be the better way to do it, but am unsure how I can implement it in R.</p>
  <p>The data is as follows:</p>
  <pre><code>   day  saleRep
  1    1 1001.104
  2   11 1000.944
  3   21 1000.734
  4   31 1000.642
  5   41 1000.517
  6   51 1000.468
  7   61 1000.425
  8   71 1000.377
  9   81 1000.286
  10  91 1000.306
  11 101 1000.285
  12 111 1000.170
  </code></pre>
  <p>Plotting it gives:
  <a href="https://i.stack.imgur.com/uPr4b.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/uPr4b.png" alt="enter image description here" /></a></p>
  <p>I am trying to achieve a few things:</p>
  <ol>
  <li>Conduct a train test split to create a forecast model that I can evaluate on the test set</li>
  <li>Using the model, obtain the predicted sale values for the <strong>250th and 500th</strong> day.</li>
  </ol>
  <p>How can I implement it in R?</p>
  <p>Use this to reproduce the code:</p>
  <pre><code>day &lt;- seq(1,111, by = 10)
  saleRep &lt;- c(1001.104, 1000.944, 1000.734, 1000.642, 1000.517, 1000.468, 1000.425, 1000.377, 1000.286, 1000.306, 1000.285, 1000.170)
  df &lt;- data.frame(day, saleRep)
  </code></pre>
  <p>Thank you.</p>