Name Date Time Series Quiz_01_2023 Score 1. What does auto-cova iance measure? A Linear dependence between multiple points on the different se ies obse ved at different times B Quadratic dependence between two points on the same se ies obse ved at different times C Linear dependence between two points on different se ies obse ved at same time D Linear dependence between two points on the same se ies obse ved at different times 2. A time se ies consists of __________. A No mathematical model B One mathematical model C Two mathematical models D Three mathematical models 3. In the semi averages method, we divide the data into ____________. A Two pa ts B Two equal pa ts C Three pa ts D Three equal pa ts 4. The second degree parabola is fitted to the time se ies when the va iations are _________. A Linear B Non linear C Random D Downward 5. The long te m trend of a time se ies graph appears to be _______. A Straight line B Second degree cu ve C Non parabolic cu ve D Parabolic cu ve or third degree cu ve 6. Which of the following is an example of seasonal va iation? A Death rate decreased due to advances in science B The sale of air conditions increasing du ing summer C Recove y in business D Stock market crashes due to outbreak of war 7. The general patte n of increase or decrease in economics or social phenomena is shown by _______________. A Seasonal trend B Cyclical trend C Secular trend D I regular trend 8. In the moving average method, which of the following trend values cannot be found? A Middle pe iods B End pe iods C Sta ting pe iods D Between extreme pe iods 9. Which of the following is not a necessa y condition for weakly stationa y time se ies? A Mean is constant and does not depend on time B Auto-cova iance function depends on time moments (s,t) only through their difference (s-t) C The time se ies under considerations is a finite va iance process D Time se ies is Gaussian 10. Given that the demand is 100 du ing October 2016, 200 in November 2016, 300 in December 2016 and 400 in Janua y 2017, what is the 3-month simple moving average for Feb ua y 2017? A 300 B 350 C 400 D Need more info mation 11. Which of the following graphs can be used to detect seasonality in time se ies data? i. Multiple box A i only B ii only C i & ii D None of the above ii. Autoco relation 12. Two time se ies are jointly stationa y if ___________. A They are both stationa y B Cross va iance function is a function only of lag h C Both (A) and (B) D None of the above 13. Consider the following set of data: 23.32, 32.33, 32.88, 28.98, 33.16, 26.33, 29.88, 32.69, 18.98, 21.23, 26.66, 29.89 . What is the lag-one sample autoco relation of the time se ies? A 0.26 B 0.52 C 0.13 D 0.07 14. Increase in the number of patients in the hospital due to heat stroke is ________. A Secular trend B I regular va iation C Seasonal va iation D Cyclical va iation 15. The systematic components of time se ies which follows regular patte n of va iations are called _______. A Signal B Noise C Additive model D Multiplicative model 16. The unsystematic sequence which follows i regular patte n of va iations is called _______. A Noise B Signal C Linear D Non-linear 17. The difference between the actual value of the time se ies and the forecasted value is known as __________. A Residual B Sum of va iation C Sum of squares of residual D All of the above 18. Which of the following is t ue for White noise? A Mean = 0 B Zero auto-cova iance C Zero auto-cova iance except at lag zero D Quadratic va iance 19. The pa tial autoco relation function PACF is necessa y for distinguishing between _______. A AR and MA model B AR and ARMA model C MA and ARMA model D different models from ARMA family 20. Second differencing in time se ies can help to eliminate which trend? A Quadratic trend B Linear trend C Both quadratic and linear trends D None of the above 21. A common method known as ratio-to-trend analysis is used to _______. A Deseasonalise data B Take moving average C Remove multi-collinea ity D Represent graphical cu ve 22. In moving average method we cannot find trend values of some ___________. A End pe iods B Middle pe iods C Sta ting and end pe iods D Sta ting pe iods 23. Seasonal va iations are _____________. A Sho t te m va iations B Long te m va iations C Sudden va iations D None of the above 24. Time se ies data have a total number of _________ components. A One B Two C Three D Four 25. Which of the following is an example of a time se ies problem? i. Estimating number of hotel room bookings in the next 6 months ii. Estimating the total sales in the next 3 years of an insurance company iii. Estimating the number of calls for the next one week A iii only B i & ii C ii & iii D i, ii & iii