Time Series Analysis Short Type Questions and Answers

(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)

(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

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

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