The following table gives monthly data for four years in million tons of consumption of ice cream in a country. Plot the series and comment on the visible seasonality and trend. Estimate the centered moving averages for this monthly series. Plot CMA and comment. Next, estimate the S,I component which only includes seasonal and irregular movements of the series. Then find the seasonal indexes for the twelve months removing the irregular component. Find the de- seasonalized levels for the series. Plot De-seasonalized Y and comment. Then estimate the trend values for the four sample years and the 12 months of the year 2020 using linear regression. Finally, make the forecast for the 12 months of 2020 using the Ratio-to-Moving Average method to capture the Trend and Seasonal patterns, using Excel. Plot the forecasted values for the 60 periods including 12 months of the year 2020. Plot the errors for in-sample periods and calculate RMSE. Comment on the error plot with respect to the existence of pattern or lack of visible pattern. Month/Year 2016 2017 2018 2019 Jan 1200 1440 1580 1925 Feb 1140 1350 1500 1835 Mar 1100 1310 1450 1770 Apr 1050 1250 1400 1685 May 900 1100 1260 1445 Jun 880 1070 1230 1415 Jul 840 900 1150 1350 Aug 860 920 1175 1380 Sep 880 950 1205 1415 Oct 950 1090 1280 1525 Nov 1050 1230 1390 1685 Dec 1150 1360 1510 1845 (Hint: First, you have to type the data in column form in Excel) Month/Year 2016 2017 2018 2019 Jan 1200 1440 1580 1925 Feb 1140 1350 1500 1835 Mar 1100 1310 1450 1770 Apr 1050 1250 1400 1685 May 900 1100 1260 1445 Jun 880 1070 1230 1415 Jul 840 900 1150 1350 Aug 860 920 1175 1380 Sep 880 950 1205 1415 Oct 950 1090 1280 1525 Nov 1050 1230 1390 1685 Dec 1150 1360 1510 1845.

1. The following table gives monthly data for four years in million tons of consumption of ice
cream in a country. Plot the series and comment on the visible seasonality and trend. Estimate
the centered moving averages for this monthly series. Plot CMA and comment. Next, estimate
the S,I component which only includes seasonal and irregular movements of the series. Then
find
the seasonal indexes for the twelve months removing the irregular component. Find the de-
seasonalized levels for the series. Plot De-seasonalized Y and comment. Then estimate the trend
values for the four sample years and the 12 months of the year 2020 using linear regression.
Finally, make the forecast for the 12 months of 2020 using the Ratio-to-Moving Average
method to capture the Trend and Seasonal patterns, using Excel. Plot the forecasted values for
the 60 periods including 12 months of the year 2020. Plot the errors for in-sample periods and
calculate RMSE. Comment on the error plot with respect to the existence of pattern or lack of
visible pattern.
Month/Year
2016
2017
2018
2019
Jan
1200
1440
1580
1925
Feb
1140
1350
1500
1835
Mar
1100
1310
1450
1770
Apr
1050

2. 1250
1400
1685
May
900
1100
1260
1445
Jun
880
1070
1230
1415
Jul
840
900
1150
1350
Aug
860
920
1175
1380
Sep
880
950
1205
1415
Oct
950
1090
1280
1525
Nov
1050
1230

3. 1390
1685
Dec
1150
1360
1510
1845
(Hint: First, you have to type the data in column form in Excel)
Month/Year
2016
2017
2018
2019
Jan
1200
1440
1580
1925
Feb
1140
1350
1500
1835
Mar
1100
1310
1450
1770
Apr
1050
1250
1400
1685
May
900

4. 1100
1260
1445
Jun
880
1070
1230
1415
Jul
840
900
1150
1350
Aug
860
920
1175
1380
Sep
880
950
1205
1415
Oct
950
1090
1280
1525
Nov
1050
1230
1390
1685
Dec
1150
1360

5. 1510
1845