Month	Region 1
Jan-18	13091
Feb-18	17679
Mar-18	22759
Apr-18	27966
May-18	27895
Jun-18	30566
Jul-18	29444
Aug-18	28364
Sep-18	28393
Oct-18	24444
Nov-18	18000
Dec-18	12453
Jan-19	12778
Feb-19	18214
Mar-19	23889
Apr-19	29455
May-19	29464
Jun-19	27414
Jul-19	27368
Aug-19	27321
Sep-19	29444
Oct-19	23774
Nov-19	17308
Dec-19	12941
Jan-20	10962
Feb-20	15273
Mar-20	20556
Apr-20	26786
May-20	24828
Jun-20	24737
Jul-20	24828
Aug-20	25179
Sep-20	24545
Oct-20	19286
Nov-20	15273
Dec-20	9107
Jan-21	8571
Feb-21	13158
Mar-21	19655
Apr-21	25179
May-21	23103
Jun-21	24286
Jul-21	24737
Aug-21	26607
Sep-21	22982
Oct-21	16897
Nov-21	13750
Dec-21	7818
Jan-22	7547
Feb-22	13889
Mar-22	18302
Apr-22	25192
May-22	24706
Jun-22	25306
Jul-22	27083
Aug-22	26042
Sep-22	26304
Oct-22	22558
Nov-22	14773
Dec-22	6977

1. Plot the time series (Sheet: Data) of your related region (Table 2).
2. Identify the adapted method for your region.
3. Make a forecasting for the region based on the adapted model
4. What are the best parameters of your proposed model ?
5. What are the parameters for the worst model ?
6. What will be the forcasted value of january 2022 ?
7. Propose a method for taking into accound the random component.
8. Based on your new model, plot your forcasted time series
9. What will be the new forcasted value of january 2022 ?
10. Comment your results