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Time Series Analysis Short Type Questions and Answers | Time Series Analysis MCQs Quiz

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Free download in PDF Time Series Analysis Multiple Choice Questions and Answers for competitive exams. These short objective type questions with answers are very important for Board exams as well as competitive exams like UPSC, NDA, SSC etc. These short solved questions or quizzes are provided by Gkseries./p>

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(1) Which of the following is not a necessary condition for weakly stationary time series?
[A] Mean is constant and does not depend on time
[B] Autocovariance function depends on s and t only through their difference |s-t| (where t and s are moments in time)
[C] The time series under considerations is a finite variance process
[D] Time series is Gaussian
Answer: Time series is Gaussian
(2) Which of the following is not a technique used in smoothing time series?
[A] Nearest Neighbour Regression
[B] Locally weighted scatter plot smoothing
[C] Tree based models like (CART)
[D] Smoothing Splines
Answer: Tree based models like (CART)

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(3) If the demand is 100 during October 2016, 200 in November 2016, 300 in December 2016, 400 in January 2017. What is the 3-month simple moving average for February 2017?
[A] 300
[B] 350
[C] 400
[D] Need more information
Answer: 300
(4) Suppose, you are a data scientist at Analytics Vidhya. And you observed the views on the articles increases during the month of Jan-Mar. Whereas the views during Nov-Dec decreases.

Does the above statement represent seasonality?

[A] TRUE
[B] FALSE
[C] Can’t Say
Answer: TRUE
(5) Which of the following graph can be used to detect seasonality in time series data?

1. Multiple box

2. Autocorrelation

[A] Only 1
[B] Only 2
[C] 1 and 2
[D] None of these
Answer: 1 and 2
(6) Stationarity is a desirable property for a time series process.
[A] TRUE
[B] FALSE
Answer: TRUE
(7) Imagine, you are working on a time series dataset. Your manager has asked you to build a highly accurate model. You started to build two types of models which are given below.

Model 1: Decision Tree model

Model 2: Time series regression model

At the end of evaluation of these two models, you found that model 2 is better than model 1. What could be the possible reason for your inference?

[A] Model 1 couldn’t map the linear relationship as good as Model 2
[B] Model 1 will always be better than Model 2
[C] You can’t compare decision tree with time series regression
[D] None of these
Answer: Model 1 couldn’t map the linear relationship as good as Model 2
(8) 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 autocorrelation of the time series?

[A] 0.26
[B] 0.52
[C] 0.13
[D] 0.07
Answer: 0.13
(9) Any stationary time series can be approximately the random superposition of sines and cosines oscillating at various frequencies.
[A] TRUE
[B] FALSE
Answer: TRUE
(10) Two time series are jointly stationary if _____ ?
[A] They are each stationary
[B] Cross variance function is a function only of lag h
[C] Only A
[D] Both A and B
Answer: Both A and B

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