title: Forecasting product return rates based on past returns
body: <p>I have a waterfall dataset which shows the returns of laptops over different weeks due to defects. For sales of each month it shows returns over different months
  <a href="https://i.stack.imgur.com/VTvmL.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/VTvmL.png" alt="returns data"></a></p>
  
  <p>I transformed the data to a weekly level: For example the returns column will be the returns of the first month for each sale month.(it will be the sum of the first diagonal
  <a href="https://i.stack.imgur.com/1MjNd.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/1MjNd.png" alt="enter image description here"></a></p>
  
  <p>I fit the pdf into a 2 parameter weibull distribution which models failure rates. (to approximate a bathtub curve)</p>
  
  <p>I then used the fit curve to estimate reliability rates form the predicted cdf. Using that the unreliability rate is predicted.</p>
  
  <p>However the Weibull distribution does not accurately model a bathtub curve which is frequent with failure rates and hence the accuracy is quite low. </p>
  
  <p>Is there a better approach to this problem of predicted return rates?</p>