FastData Inc. recently launched a new subscription-based analytics tool. The monthly subscriber counts (in thousands) for the first 15 months are shown below:\table[[Month,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],[Subscribers (000s),12,13,14,14,16,17,18,17,19,20,21,22,21,22,23]]FastData currently uses a averaging forecasting method, which simply assumes that the forecasting will be the average of the past. That is,hat(y)16=17.93333.However, this company hired your professor to improve their model. However, your professor outsources the work to you because he believes that you can do it! The task is clear: Use a different forecasting method (e.g., average, moving average, exponential smoothing, linear regression, etc.) to forecast subscriber count for Month 16.(a)(5 points) Show your steps and prove that your method is better.(b)(1 point) Explain, what forecast do you get using your chosen method? Why is it better (or not)?No need to de-season.