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