title: anomaly detection in time series
body: <p>I have the data set below:</p>
  <p><a href="https://i.stack.imgur.com/QT8Tc.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/QT8Tc.jpg" alt="enter image description here" /></a></p>
  <p>First column is date(weekly frequency), 2th column is name of commercial banks, 3rd one is identification of bonds. A branch (B branches) values are amount of bonds that held by A branches(B branches) of Banks. I wonder if any outlier can be detected the amounts of bonds held by branches.</p>
  <p>Which algorithms can be used for this data set in python.</p>
  <p>Note:</p>
  <ul>
  <li>not all bonds are included in every bank.</li>
  <li>a bond can be held in a branch of a bank but may not be in the other.</li>
  <li>a bond may come out of the data set because it is amortized.</li>
  <li>a newly issued bond can enter data set.</li>
  </ul>
  <p>Any help will be appreciated.</p>