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

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.

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.

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.

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.

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.

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.

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.

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.