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ML_Techniques_I Week 3 Session 1

Authored by Samiratu Ntohsi

Information Technology (IT)

University

Used 1+ times

ML_Techniques_I Week 3 Session 1
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8 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

An exogenous variable in forecasting is one that is

Determined inside the system and influences itself

Always a lag of the target variable

Independent of other variables in the system but affects the output

Ignored during model fitting

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

A 1-D Convolutional Neural Network (CNN) is especially useful for forecasting because it can:

Guarantee stationarity

Extract local temporal patterns with shared filters

Learn global hierarchical relationships

Model infinite long-range dependencie

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

The term forecast horizon refers to:

The interval between observations

The length of the training window

The number of features in the model

How far into the future predictions are required

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Creating lag features and rolling means for a univariate series is an example of

Feature engineering steps

Dimensionality reduction

Hyper-parameter optimisation

Differencing

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Multivariate time-series forecasting involves

Modelling only seasonal components

Predicting many future horizons of one series

Using multiple correlated variables to predict one or more targets

Clustering similar sequences without a target

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which property must a series satisfy for most classical statistical models (like ARIMA to be directly applicable without transformation?

Normality

Weak stationarity

Uniform spacing

High frequency

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Seasonal differencing is most often used to

Stabilise variance

Remove linear trend

Eliminate repeating seasonal patterns

Create exogenous regressors

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