You will use the major forecasting techniques learned in this course to build the forecasting models for your revenue data.

Please answer the questions below based on the picture I attached and the Date/Revenue chart.

Simple exponential smoothing

a.State the reason for your choice of the smoothing model and run the model. Make sure to store the residuals.

d.Show the resulting graph from the smoothing method. State the model you picked and the parameter values such as ?, ?, ?, if any.

c.Examine the residuals using time series plot and ACF graph. Do you observe any significant autocorrelation?

d.Briefly discuss whether you think the model can do a good job forecasting and why.

Autocorrelation Function for Revenue (with 5% significance limits for the autocorrelations) 1.0 D.8 0.6 0.4 0.2 Autocorrelation 0.0 -0.2 -0.4 -0.6 -0.8 -1.0 2 6 8 10 12 14 16 18 20 22 24 26 28 LagSmoothing Plot for Revenue Single Exponential Method 35000 Variable Actual 30000 Fits Smoothing Constant 25000 a 0.738697 Accuracy Measures 20000 MAPE 4 Revenue MAD 701 MSD 1749221 15000 10000 5000 1 11 22 33 44 55 66 77 88 99 110 Index35000 30000 25000 Revenue 20000 15000 10000 Time Series Plot of Revenue 5000 1990-03-30 1992-09-30 1995-06-30 1998-03-31 2000-12-31 2003-09-30 2006-06-30 Date 2009-03-31 2011-12-31 2014-09-30 2017-06-30