CHRIST (Deemed to be University), Bangalore – 560 029
Department of Statistics and Data Science
END SEMESTER EXAMINATION – April/May 2024
UG VI Semester
Programme Name: B.Sc. [CMS & EMS] Max. Marks: 100
Course Name: Time Series Analysis and Forecasting Techniques Time: 3 Hrs
Course Code: STA631
General Instructions
All rough work should be done in the answer script. Do not write or scribble in the
question paper except your register number.
Verify the Course code / Course title & number of pages of questions in the question
paper.
Make sure your mobile phone is switched off and placed at the designated place in the
hall.
Malpractices will be viewed very seriously.
Answers should be written on both sides of the paper in the answer booklet. No sheets
should be detached from the answer booklet.
Answers without the question numbers clearly indicated will not be valued. No page
should be left blank in the middle of the answer booklet.
Course Outcomes (COs): The students will able to
CO1: Demonstrate the approach and analyze Univariate time series.
CO2: Infer the difference between various time series models like AR, MA, ARMA and
ARIMA models.
CO3: Demonstrate the difference between stationary and non-stationary time series
models.
CO4: Demonstrate how to forecast future observations of the time series.
Section A
Answer ALL the questions 10 x 3 marks = 30
Q. Question CO RBT
No
1. Explain about short term components of time series. 1 L2
2. Show that the polynomial of order p becomes a constant after 1 L5
differencing p times.
3. Explain different stationary processes of a stochastic process. 1 L1
4. Differentiate between PACF and ACF of a linear time series. 2 L3
5. Explain an MA(q) model. Discuss its stationarity behaviour. 2 L5
6. How will you determine the order of AR(2) and MA(3) model? 2 L4
7. Explain the portmanteau test. 3 L1
8. Explain Shapiro Wilk test. 3 L2
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9. What is non-stationarity? Explain how to detect the non-stationarity 4 L5
in any time series data.
10. Are ARIMA models useful for real life data sets? Briefly explain 4 L4
with examples.
Section B
Answer ALL the questions 5 x 6 marks = 30
Q. Question CO RBT
No.
11. Explain Simple exponential forecasting in time series. 1 L2
12. Describe the method of Conditional Maximum likelihood 3 L3
estimation of AR(1) Process.
13. Obtain the stationarity condition of an AR(p) process. 2 L5
14. Explain the Yule Walker equations in AR(3) process with ρ1=0.1, 3 L6
ρ2=0.2, ρ3=0.4.
15. a) Explain the MMSE forecast and its properties. 4 L1
OR
b) Describe about the difference equation form of ARIMA model.
Section C
Answer ALL the questions 4 x 10 marks = 40
Q. Question CO RBT
No.
16. a) Describe the residual analysis in time series. 3 L2
OR
b) Describe different estimation methods used in ARMA models.
17. a) Explain ARMA(2,1) process. Obtain its mean, variance and ACF. 2 L1
OR
b) Derive the mean, variance, ACF and stationarity condition for
AR(p) model.
18. a) Explain elimination of trend in the absence of seasonality. 1 L5
OR
b) Describe trend fitting by exponential smoothing.
a) Describe forecasting of stationary AR(1) model. Also obtain its 4 L4
19. forecast error and forecast variance.
OR
b) Describe unit root process and explain the method of testing the
same.
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Revised Bloom’s Taxonomy (RBT) Levels:
L1 – Remembering L2 – Understanding L3 – Applying
L4 – Analyzing L5 – Evaluating L6 - Creating
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