HISTORICAL DATA Date Time Daily Cases Total Cases Total Deaths Death Rate Daily Deaths 29 Jun 1 44,734 26,81,811 1,29,358 4 82 366 30 Jun 2 48,402 27,30,213 1,30,085 4 76 727 01 Jul 3 52,537 27,82,750 1,30,996 4 71 911 02 Jul 4 57,685 28,40,435 1,31,689 4 64 693 03 Jul 5 59,776 29,00,211 1,32,320 4 56 631 04 Jul 6 50,134 29,50,345 1,32,590 4 49 270 05 Jul 7 46,214 29,96,559 1,32,858 4 43 268 DATA FOR FORECAST MODELS Date Time Daily Cases Total Cases Total Deaths Death Rate Daily Deaths 06 Jul 1 50,929 30,47,488 1,33,246 4 37 388 07 Jul 2 56,025 31,03,513 1,34,252 4 33 1,006 08 Jul 3 62,465 31,65,978 1,35,162 4 27 910 09 Jul 4 61,739 32,27,717 1,36,143 4 22 981 10 Jul 5 72,460 33,00,177 1,37,005 4 15 862 11 Jul 6 62,182 33,62,359 1,37,751 4 10 746 12 Jul 7 58,781 34,21,140 1,38,140 4 04 389 13 Jul 8 65,956 34,87,096 1,38,619 3 98 479 14 Jul 9 66,153 35,53,249 1,39,576 3 93 957 15 Jul 10 99,178 36,52,427 1,40,599 3 85 1,023 16 Jul 11 46,567 36,98,994 1,41,581 3 83 982 17 Jul 12 75,166 37,74,160 1,42,551 3 78 970 18 Jul 13 63,382 38,37,542 1,43,376 3 74 825 19 Jul 14 65,452 39,02,994 1,43,798 3 68 422 20 Jul 15 63,080 39,66,074 1,44,338 3 64 540 21 Jul 16 67,647 40,33,721 1,45,526 3 61 1,188 22 Jul 17 72,140 41,05,861 1,46,759 3 57 1,233 23 Jul 18 70,060 41,75,921 1,47,955 3 54 1,196 24 Jul 19 78,586 42,54,507 1,49,121 3 51 1,166 25 Jul 20 68,344 43,22,851 1,50,047 3 47 926 26 Jul 21 56,721 43,79,572 1,50,509 3 44 462 27 Jul 22 61,960 44,41,532 1,51,106 3 40 597 28 Jul 23 65,209 45,06,741 1,52,436 3 38 1,330 29 Jul 24 65,463 45,72,204 1,53,901 3 37 1,465 30 Jul 25 68,745 46,40,949 1,55,751 3 36 1,850 31 Jul 26 72,577 47,13,526 1,57,198 3 34 1,447 01 Aug 27 58,674 47,72,200 1,58,233 3 32 1,035 02 Aug 28 49,756 48,21,956 1,58,658 3 29 425 03 Aug 29 48,769 48,70,725 1,59,262 3 27 604 04 Aug 30 54,705 49,25,430 1,60,674 3 26 1,412 05 Aug 31 55,205 49,80,635 1,62,007 3 25 1,333 06 Aug 32 58,951 50,39,586 1,63,236 3 24 1,229 07 Aug 33 63,470 51,03,056 1,64,569 3 22 1,333 08 Aug 34 56,171 51,59,227 1,65,585 3 21 1,016 09 Aug 35 48,028 52,07,255 1,66,113 3 19 528 10 Aug 36 49,857 52,57,112 1,66,666 3 17 553 11 Aug 37 54,576 53,11,688 1,68,167 3 17 1,501 12 Aug 38 54,402 53,66,090 1,69,573 3 16 1,406 13 Aug 39 54,797 54,20,887 1,70,878 3 15 1,305 14 Aug 40 60,032 54,80,919 1,72,049 3 14 1,171 15 Aug 41 53,580 55,34,499 1,73,157 3 13 1,108 16 Aug 42 37,698 55,72,197 1,73,684 3 12 527 17 Aug 43 40,576 56,12,773 1,74,255 3 10 571 18 Aug 44 44,015 56,56,788 1,75,649 3 11 1,394 19 Aug 45 44,973 57,01,761 1,76,933 3 10 1,284 20 Aug 46 45,357 57,47,118 1,78,030 3 10 1,097 21 Aug 47 50,481 57,97,599 1,79,200 3 09 1,170 22 Aug 48 43,829 58,41,428 1,80,174 3 08 974 23 Aug 49 32,718 58,74,146 1,80,604 3 07 430 COVID 19 Case Infection Forecast (10 pts) Use the COVID 19 case infection data (provided on sheet P8 Data in the excel template) to conduct a forecast analysis to determine the best exponential smoothing model to forecast COVID 19 Daily Case Infections Analyze the 3 different exponential smoothing techniques (Simple, Holtz and Winters methods) on the data for forecast models Use all of the daily case infection data (column c) from July 6 th through August 23 rd , for the analysis Use the Historical Data (data from June 29 th thru July 5 th ) to estimate the Smooth Trend and Seasonal Factors that are to be utilized in the Holtz and Winters Methods For the Smooth Level initial value, (L 1 for the simplex method, and L 0 for the Holtz and Winters method), use the historical data of daily case infections from July 5 th value which is 46,214 case infections Estimate a Smooth Trend (To) value by taking the average trend from the historical data Estimate the seasonal factors Si, using the historical data over the 7 day period Formulate models for the 3 methods (exponential smoothing, holts and winters) and answer the following questions What are the optimum alpha, beta and gamma values for the three different models What is the minimum MAD values for each of the models What is the August 24 th forecast for daily case infections that day, from the three different models Which of the 3 model provides the best estimation of forecast for daily case infections Why Show a graph of Actual versus forecast for each model

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HISTORICAL DATA Date Time Daily Cases Total Cases Total Deaths Death Rate Daily Deaths 29-Jun 1 44,734 26,81,811 1,29,358 4.82% 366 30-Jun 2 48,402 27,30,213

HISTORICAL DATA
Date	Time	Daily Cases	Total Cases	Total Deaths	Death Rate	Daily Deaths
29-Jun	1	44,734	26,81,811	1,29,358	4.82%	366
30-Jun	2	48,402	27,30,213	1,30,085	4.76%	727
01-Jul	3	52,537	27,82,750	1,30,996	4.71%	911
02-Jul	4	57,685	28,40,435	1,31,689	4.64%	693
03-Jul	5	59,776	29,00,211	1,32,320	4.56%	631
04-Jul	6	50,134	29,50,345	1,32,590	4.49%	270
05-Jul	7	46,214	29,96,559	1,32,858	4.43%	268

DATA FOR FORECAST MODELS
Date	Time	Daily Cases	Total Cases	Total Deaths	Death Rate	Daily Deaths
06-Jul	1	50,929	30,47,488	1,33,246	4.37%	388
07-Jul	2	56,025	31,03,513	1,34,252	4.33%	1,006
08-Jul	3	62,465	31,65,978	1,35,162	4.27%	910
09-Jul	4	61,739	32,27,717	1,36,143	4.22%	981
10-Jul	5	72,460	33,00,177	1,37,005	4.15%	862
11-Jul	6	62,182	33,62,359	1,37,751	4.10%	746
12-Jul	7	58,781	34,21,140	1,38,140	4.04%	389
13-Jul	8	65,956	34,87,096	1,38,619	3.98%	479
14-Jul	9	66,153	35,53,249	1,39,576	3.93%	957
15-Jul	10	99,178	36,52,427	1,40,599	3.85%	1,023
16-Jul	11	46,567	36,98,994	1,41,581	3.83%	982
17-Jul	12	75,166	37,74,160	1,42,551	3.78%	970
18-Jul	13	63,382	38,37,542	1,43,376	3.74%	825
19-Jul	14	65,452	39,02,994	1,43,798	3.68%	422
20-Jul	15	63,080	39,66,074	1,44,338	3.64%	540
21-Jul	16	67,647	40,33,721	1,45,526	3.61%	1,188
22-Jul	17	72,140	41,05,861	1,46,759	3.57%	1,233
23-Jul	18	70,060	41,75,921	1,47,955	3.54%	1,196
24-Jul	19	78,586	42,54,507	1,49,121	3.51%	1,166
25-Jul	20	68,344	43,22,851	1,50,047	3.47%	926
26-Jul	21	56,721	43,79,572	1,50,509	3.44%	462
27-Jul	22	61,960	44,41,532	1,51,106	3.40%	597
28-Jul	23	65,209	45,06,741	1,52,436	3.38%	1,330
29-Jul	24	65,463	45,72,204	1,53,901	3.37%	1,465
30-Jul	25	68,745	46,40,949	1,55,751	3.36%	1,850
31-Jul	26	72,577	47,13,526	1,57,198	3.34%	1,447
01-Aug	27	58,674	47,72,200	1,58,233	3.32%	1,035
02-Aug	28	49,756	48,21,956	1,58,658	3.29%	425
03-Aug	29	48,769	48,70,725	1,59,262	3.27%	604
04-Aug	30	54,705	49,25,430	1,60,674	3.26%	1,412
05-Aug	31	55,205	49,80,635	1,62,007	3.25%	1,333
06-Aug	32	58,951	50,39,586	1,63,236	3.24%	1,229
07-Aug	33	63,470	51,03,056	1,64,569	3.22%	1,333
08-Aug	34	56,171	51,59,227	1,65,585	3.21%	1,016
09-Aug	35	48,028	52,07,255	1,66,113	3.19%	528
10-Aug	36	49,857	52,57,112	1,66,666	3.17%	553
11-Aug	37	54,576	53,11,688	1,68,167	3.17%	1,501
12-Aug	38	54,402	53,66,090	1,69,573	3.16%	1,406
13-Aug	39	54,797	54,20,887	1,70,878	3.15%	1,305
14-Aug	40	60,032	54,80,919	1,72,049	3.14%	1,171
15-Aug	41	53,580	55,34,499	1,73,157	3.13%	1,108
16-Aug	42	37,698	55,72,197	1,73,684	3.12%	527
17-Aug	43	40,576	56,12,773	1,74,255	3.10%	571
18-Aug	44	44,015	56,56,788	1,75,649	3.11%	1,394
19-Aug	45	44,973	57,01,761	1,76,933	3.10%	1,284
20-Aug	46	45,357	57,47,118	1,78,030	3.10%	1,097
21-Aug	47	50,481	57,97,599	1,79,200	3.09%	1,170
22-Aug	48	43,829	58,41,428	1,80,174	3.08%	974
23-Aug	49	32,718	58,74,146	1,80,604	3.07%	430

COVID-19 Case Infection Forecast (10 pts) -Use the COVID-19 case infection data (provided on sheet P8-Data in the excel template) to conduct a forecast analysis to determine the best exponential smoothing model to forecast COVID-19 Daily Case Infections. Analyze the 3 different exponential smoothing techniques (Simple, Holtz and Winters methods) on the data for forecast models. Use all of the daily case infection data (column c) from July 6^th through August 23^rd, for the analysis. Use the Historical Data (data from June 29^th thru July 5^th) to estimate the Smooth Trend and Seasonal Factors that are to be utilized in the Holtz and Winters Methods. For the Smooth Level initial value, (L₁ for the simplex method, and L₀ for the Holtz and Winters method), use the historical data of daily case infections from July 5^th value which is 46,214 case infections.Estimate a Smooth Trend (To) value by taking the average trend from the historical data. Estimate the seasonal factors Si, using the historical data over the 7 day period. Formulate models for the 3 methods (exponential smoothing, holts and winters) and answer the following questions?

What are the optimum alpha, beta and gamma values for the three different models?
What is the minimum MAD values for each of the models?
What is the August 24^th forecast for daily case infections that day, from the three different models?
Which of the 3 model provides the best estimation of forecast for daily case infections? Why?
Show a graph of Actual versus forecast for each model