Data of 40 previous months of Sales is given to you. You have to choose the right ARIMA model for forecasting [10 marks] a) Analyze the ACF of the data given to you. What can you say about the stationarity of the signal? (Generate upto 10 ACF values) B) After doing first order difference, d=1, what is the ACF and PACF of the difference data? (Generate upto 10 ACF values and 5 PACF values) c) Compare ACF and PACF to limits and determine the two models yqy want to compare If we choose to compare ARIMA(1,1,0) and ARIMA(0,1,1) d) Perform AR forecasting and calculate RMSE e) If Omega for ARIMA(0,1,1) is given to you as -0.8952, what can you say about the RMSE of the ARIMA(0,1,1) model? 1) Plot forecasted data with ARIMA(1,1.0) and ARIMA(0.1.1) along with original Sales data *Answers to be shown in blue color cells Time 1 2. Sales (in Lakhs of Rupees) 1029.438793 1020.820478 1028.787517 1055.163527 1037.808807 3 4 5 Time 1 2 3 4. Sales (in Lakhs of Rupees) 1029.438793 1020.820478 1028.787517 1055.163527 1037.808807 5 1063.064629 6 7 1078.729814 8 9 10 11 1083.633749 1111.573695 1115.824212 1124.451281 1093.217734 1120.670497 1117.190658 1112.49033 12 13 14 15 15 1112.49033 16 17 18 1142.399646 1165.38174 1172.883281 1149.200955 1185.193568 1203.39663 19 20 21 22 1204.549766 1205.417637 23 24 25 26 27 1179.813551 1234.813027 1205.286458 1239.840217 1238.820132 1227.921686 28 29 30 1251.091524 30 31 32 1251.091524 1270.304925 1268.943442 1268.80021 1253.084106 1268.048835 33 34 35 36 37 1281.344018 38 1297.397506 1313.413927 1312.66472 39 40 1335.79445