Use GM file data that is listed below GM data is a 5 year stock price and trade volume for General Motors Company (GM) from May 2016 through April 2021 This data includes average daily open, high, low, and close price of the month Adj close column applies adjustments on close price due to splits and dividends Volume represents the shares traded during the month a Which one of the following forecasting methods works best for High price of the next month based on MAD criterion if you use the entire available data set b What is your next month forecast of GM High stock price using the best method you found in question 1 c What is your next month forecast for GM adjusted close stock price (Adj Close) using double exponential smoothing with the best smoothing constants Check all combinations of smoothing factors alpha and beta for values between 0 1 and 1, incrementing 0 1 at a time to find the best value d What is your next month forecast for GM adjusted close stock price (Adj Close) using additive seasonality without a trend Check all combinations of smoothing factors alpha and gamma for values between 0 1 and 1, incrementing 0 1 at a time to find the best value e What is your next month forecast for GM adjusted close stock price (Adj Close) using Multiplicative seasonality without a trend Check all combinations of smoothing factors alpha and gamma for values between 0 1 and 1, incrementing 0 1 at a time to find the best value f What is your next month forecast for GM adjusted close stock price (Adj Close) using Excel function FORECAST ETS with seasonality g What is your next month forecast for GM adjusted close stock price (Adj Close) using simple regression use only partial data starting from March 2020 h Based on the results from all forecasting methods you used for GM adjusted close stock price (Adj Close) data, which one is the best method based on the MAPE criterion Date Open High Low Close Adj Close Volume 5 1 2016 32 05 32 12 30 17 31 28 26 41028 210049700 6 1 2016 31 15 31 15 27 34 28 3 23 89422 303560000 7 1 2016 28 69 32 87 27 52 31 54 26 96882 295969400 8 1 2016 31 54 32 09 29 82 31 92 27 29375 262310700 9 1 2016 31 86 32 55 30 13 31 77 27 16548 299362600 10 1 2016 31 64 33 08 30 88 31 6 27 34362 244472800 11 1 2016 31 7 34 98 30 21 34 53 29 87895 314369400 12 1 2016 34 81 37 74 34 67 34 84 30 1472 310509400 1 1 2017 34 98 38 38 34 84 36 61 32 02243 287500800 2 1 2017 36 68 38 1 34 72 36 84 32 22361 287888900 3 1 2017 37 2 38 55 33 79 35 36 30 92907 397935200 4 1 2017 35 36 35 4 33 37 34 64 30 6093 249493100 5 1 2017 34 63 34 65 31 92 33 93 29 98192 322113200 6 1 2017 34 06 35 05 33 05 34 93 30 86556 260038700 7 1 2017 34 52 36 63 34 45 35 98 32 1482 204852000 8 1 2017 35 81 36 74 34 59 36 54 32 64856 193493000 9 1 2017 36 73 40 69 36 62 40 38 36 0796 275318700 10 1 2017 41 01 46 76 40 8 42 98 38 79405 358941400 11 1 2017 42 72 45 35 41 35 43 09 38 89333 270788400 12 1 2017 43 19 43 42 40 7 40 99 36 99786 233305300 1 1 2018 41 24 45 52 41 15 42 41 38 62781 278088700 2 1 2018 42 12 43 56 39 32 39 35 35 8407 267909800 3 1 2018 39 59 39 59 34 5 36 34 33 09914 288911100 4 1 2018 36 1 39 67 35 23 36 74 33 80384 221705000 5 1 2018 36 76 42 77 35 28 42 7 39 28752 236116800 6 1 2018 42 98 45 39 37 39 4 36 25126 279572100 7 1 2018 39 08 40 5 36 26 37 91 35 18469 229946700 8 1 2018 37 89 38 32 35 21 36 05 33 45839 226191400 9 1 2018 36 36 49 33 28 33 67 31 24949 241380000 10 1 2018 34 3 37 05 30 56 36 59 34 32923 389476100 11 1 2018 36 24 38 75 33 85 37 95 35 6052 266802600 12 1 2018 39 39 61 31 46 33 45 31 38324 227541200 1 1 2019 32 85 39 14 32 2 39 02 36 99402 245901200 2 1 2019 38 91 40 74 38 01 39 48 37 43014 180097400 3 1 2019 39 85 41 5 36 26 37 1 35 17371 179459600 4 1 2019 37 4 40 45 37 33 38 95 37 29414 159149400 5 1 2019 39 39 2 33 15 33 34 31 92263 219724700 6 1 2019 33 35 38 99 33 08 38 53 36 89199 175430400 7 1 2019 38 93 41 37 58 40 34 39 03896 150765800 8 1 2019 41 26 41 9 35 58 37 09 35 89378 151235200 9 1 2019 36 89 39 78 36 44 37 48 36 2712 170562500 10 1 2019 37 47 38 7 33 71 37 16 36 32246 212810900 11 1 2019 37 21 38 96 34 59 36 35 18861 191233100 12 1 2019 36 01 37 62 34 96 36 6 35 77509 172043000 1 1 2020 37 37 38 32 97 33 39 32 98758 206959200 2 1 2020 33 56 35 79 29 33 30 5 30 13241 246636600 3 1 2020 30 52 32 3 14 33 20 78 20 52956 493637100 4 1 2020 19 5 24 99 17 79 22 29 22 29 359999200 5 1 2020 21 65 28 83 20 12 25 88 25 88 347689800 6 1 2020 25 99 31 66 24 25 25 3 25 3 350991100 7 1 2020 25 3 27 33 23 33 24 89 24 89 307217800 8 1 2020 25 06 31 12 24 82 29 63 29 63 273334700 9 1 2020 29 53 33 33 28 24 29 59 29 59 341929300 10 1 2020 29 52 38 03 29 15 34 53 34 53 309217200 11 1 2020 35 12 46 71 34 23 43 84 43 84 380303700 12 1 2020 44 78 45 45 40 06 41 64 41 64 274333300 1 1 2021 41 95 56 97 40 04 50 68 50 68 628903000 2 1 2021 51 46 57 05 49 35 51 33 51 33 418988300 3 1 2021 52 41 62 24 50 25 57 46 57 46 515673800 4 1 2021 57 92 63 44 57 04 60 16 60 16 130273100

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Use GM file data that is listed below. GM data is a 5-year stock price and trade volume for General Motors Company (GM) from May

Use GM file data that is listed below. GM data is a 5-year stock price and trade volume for General Motors Company (GM) from May 2016 through April 2021. This data includes average daily open, high, low, and close price of the month. “Adj close” column applies adjustments on close price due to splits and dividends. Volume represents the shares traded during the month.

a. Which one of the following forecasting methods works best for “High” price of the next month based on MAD criterion if you use the entire available data set? b. What is your next month forecast of GM “High” stock price using the best method you found in question 1?

c. What is your next month forecast for GM adjusted close stock price (Adj Close) using double exponential smoothing with the best smoothing constants. Check all combinations of smoothing factors alpha and beta for values between 0.1 and 1, incrementing 0.1 at a time to find the best value.

d. What is your next month forecast for GM adjusted close stock price (Adj Close) using additive seasonality without a trend? Check all combinations of smoothing factors alpha and gamma for values between 0.1 and 1, incrementing 0.1 at a time to find the best value.

e. What is your next month forecast for GM adjusted close stock price (Adj Close) using Multiplicative seasonality without a trend? Check all combinations of smoothing factors alpha and gamma for values between 0.1 and 1, incrementing 0.1 at a time to find the best value.

f. What is your next month forecast for GM adjusted close stock price (Adj Close) using Excel function FORECAST.ETS with seasonality?

g. What is your next month forecast for GM adjusted close stock price (Adj Close) using simple regression? use only partial data starting from March 2020 h. Based on the results from all forecasting methods you used for GM adjusted close stock price (Adj Close) data, which one is the best method based on the MAPE criterion?

Date	Open	High	Low	Close	Adj Close	Volume
5/1/2016	32.05	32.12	30.17	31.28	26.41028	210049700
6/1/2016	31.15	31.15	27.34	28.3	23.89422	303560000
7/1/2016	28.69	32.87	27.52	31.54	26.96882	295969400
8/1/2016	31.54	32.09	29.82	31.92	27.29375	262310700
9/1/2016	31.86	32.55	30.13	31.77	27.16548	299362600
10/1/2016	31.64	33.08	30.88	31.6	27.34362	244472800
11/1/2016	31.7	34.98	30.21	34.53	29.87895	314369400
12/1/2016	34.81	37.74	34.67	34.84	30.1472	310509400
1/1/2017	34.98	38.38	34.84	36.61	32.02243	287500800
2/1/2017	36.68	38.1	34.72	36.84	32.22361	287888900
3/1/2017	37.2	38.55	33.79	35.36	30.92907	397935200
4/1/2017	35.36	35.4	33.37	34.64	30.6093	249493100
5/1/2017	34.63	34.65	31.92	33.93	29.98192	322113200
6/1/2017	34.06	35.05	33.05	34.93	30.86556	260038700
7/1/2017	34.52	36.63	34.45	35.98	32.1482	204852000
8/1/2017	35.81	36.74	34.59	36.54	32.64856	193493000
9/1/2017	36.73	40.69	36.62	40.38	36.0796	275318700
10/1/2017	41.01	46.76	40.8	42.98	38.79405	358941400
11/1/2017	42.72	45.35	41.35	43.09	38.89333	270788400
12/1/2017	43.19	43.42	40.7	40.99	36.99786	233305300
1/1/2018	41.24	45.52	41.15	42.41	38.62781	278088700
2/1/2018	42.12	43.56	39.32	39.35	35.8407	267909800
3/1/2018	39.59	39.59	34.5	36.34	33.09914	288911100
4/1/2018	36.1	39.67	35.23	36.74	33.80384	221705000
5/1/2018	36.76	42.77	35.28	42.7	39.28752	236116800
6/1/2018	42.98	45	39.37	39.4	36.25126	279572100
7/1/2018	39.08	40.5	36.26	37.91	35.18469	229946700
8/1/2018	37.89	38.32	35.21	36.05	33.45839	226191400
9/1/2018	36	36.49	33.28	33.67	31.24949	241380000
10/1/2018	34.3	37.05	30.56	36.59	34.32923	389476100
11/1/2018	36.24	38.75	33.85	37.95	35.6052	266802600
12/1/2018	39	39.61	31.46	33.45	31.38324	227541200
1/1/2019	32.85	39.14	32.2	39.02	36.99402	245901200
2/1/2019	38.91	40.74	38.01	39.48	37.43014	180097400
3/1/2019	39.85	41.5	36.26	37.1	35.17371	179459600
4/1/2019	37.4	40.45	37.33	38.95	37.29414	159149400
5/1/2019	39	39.2	33.15	33.34	31.92263	219724700
6/1/2019	33.35	38.99	33.08	38.53	36.89199	175430400
7/1/2019	38.93	41	37.58	40.34	39.03896	150765800
8/1/2019	41.26	41.9	35.58	37.09	35.89378	151235200
9/1/2019	36.89	39.78	36.44	37.48	36.2712	170562500
10/1/2019	37.47	38.7	33.71	37.16	36.32246	212810900
11/1/2019	37.21	38.96	34.59	36	35.18861	191233100
12/1/2019	36.01	37.62	34.96	36.6	35.77509	172043000
1/1/2020	37	37.38	32.97	33.39	32.98758	206959200
2/1/2020	33.56	35.79	29.33	30.5	30.13241	246636600
3/1/2020	30.52	32.3	14.33	20.78	20.52956	493637100
4/1/2020	19.5	24.99	17.79	22.29	22.29	359999200
5/1/2020	21.65	28.83	20.12	25.88	25.88	347689800
6/1/2020	25.99	31.66	24.25	25.3	25.3	350991100
7/1/2020	25.3	27.33	23.33	24.89	24.89	307217800
8/1/2020	25.06	31.12	24.82	29.63	29.63	273334700
9/1/2020	29.53	33.33	28.24	29.59	29.59	341929300
10/1/2020	29.52	38.03	29.15	34.53	34.53	309217200
11/1/2020	35.12	46.71	34.23	43.84	43.84	380303700
12/1/2020	44.78	45.45	40.06	41.64	41.64	274333300
1/1/2021	41.95	56.97	40.04	50.68	50.68	628903000
2/1/2021	51.46	57.05	49.35	51.33	51.33	418988300
3/1/2021	52.41	62.24	50.25	57.46	57.46	515673800
4/1/2021	57.92	63.44	57.04	60.16	60.16	130273100

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To address your questions lets start by loading the provided data into a Python environment and then proceed with the analysis import pandas as pd imp... blur-text-image