1 Q. 2. (10 Marks): Consider production data for 10 months Month Production 21123 2 24712 3 29619 31151 5 34859 35152 42375 8 50839 61581 10 61951 4 6 7 9 The graph (Production vs Month) and ACF are as follows Chart Title 201001 60000 SCIUT 40005 3C1U01 20000 1 2 3 4 5 1 9 15 ACF - Concert Lower confidence Lag Number 1. Does the above time series have irregular component? (1 Mark) 2. Does the above time series have trend? If yes, is it increasing or decreasing? (1 Marks) 3. Does the above time series have seasonality? If yes, at what time lag? (1 Mark) 4. Which exponential smoothening method will you try? Why? (2 Marks) 5. If one is applying ARIMA model, what will be the values of d. P.D. Q? Answer whether it will be zero / not zero (2 Marks) 6. What will be forecast for Month 11, using NF1, MA (3) and MA (6)? (3 Marks) 1 Q. 2. (10 Marks): Consider production data for 10 months Month Production 21123 2 24712 3 29619 31151 5 34859 35152 42375 8 50839 61581 10 61951 4 6 7 9 The graph (Production vs Month) and ACF are as follows Chart Title 201001 60000 SCIUT 40005 3C1U01 20000 1 2 3 4 5 1 9 15 ACF - Concert Lower confidence Lag Number 1. Does the above time series have irregular component? (1 Mark) 2. Does the above time series have trend? If yes, is it increasing or decreasing? (1 Marks) 3. Does the above time series have seasonality? If yes, at what time lag? (1 Mark) 4. Which exponential smoothening method will you try? Why? (2 Marks) 5. If one is applying ARIMA model, what will be the values of d. P.D. Q? Answer whether it will be zero / not zero (2 Marks) 6. What will be forecast for Month 11, using NF1, MA (3) and MA (6)