Data Science - Time Series Forecasting with Facebook Prophet in Python - Walk-Forward Validation

Data Science - Time Series Forecasting with Facebook Prophet in Python - Walk-Forward Validation

Assessment

Interactive Video

Computers

11th - 12th Grade

Hard

Created by

Quizizz Content

FREE Resource

The video tutorial introduces walk forward validation as a suitable method for time series data, contrasting it with traditional train-test splits and K-fold cross validation, which are inadequate due to time dependencies. Walk forward validation involves incrementally training models with past data to predict future outcomes, reflecting real-world scenarios. The tutorial also discusses practical considerations, such as step sizes and data windowing, and highlights limitations of using Psykitlearn for time series validation.

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7 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is a single train-test split not ideal for time series data?

It mixes future data with past data.

It leads to overfitting on the test set.

It requires a time machine to implement.

It does not allow for parameter optimization.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the main reason K-fold cross-validation is unsuitable for time series data?

It is only applicable to Scikit-learn models.

It does not account for time dependency among data points.

It requires too much computational power.

It uses overlapping validation sets.

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How does walk forward validation differ from traditional cross-validation?

It requires a constant size window for training.

It uses future data to train the model.

It does not allow for any validation.

It trains the model on all available data and predicts future data.

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a potential advantage of using a constant size window in walk forward validation?

It allows for the use of future data.

It ensures that all past data is used.

It adapts to changing dependencies in the time series.

It simplifies the validation process.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a limitation of using time series split in Scikit-learn?

It only works with Facebook Prophet models.

It forces the use of non-overlapping blocks.

It requires a time machine.

It allows for variable block sizes.

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why might a step size of one be more realistic in time series validation?

It allows for the use of future data.

It ensures all data is used equally.

It reflects the ability to update the model after every time step.

It simplifies the validation process.

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a common constraint when using time series split in Scikit-learn?

It allows for overlapping validation sets.

It requires a constant size window.

All blocks must be of equal size.

It can only be used with non-time series data.