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  3. Machine Learning for Time Series Data in Python

Exercise

Cross-validation without shuffling

Now, re-run your model fit using block cross-validation (without shuffling all datapoints). In this case, neighboring time-points will be kept close to one another. How do you think the model predictions will look in each cross-validation loop?

An instance of the Linear regression model object is available in your workspace. Also, the arrays X and y (training data) are available too.

Instructions

100 XP
  • Instantiate another cross-validation object, this time using KFold cross-validation with 10 splits and no shuffling.
  • Iterate through this object to fit a model using the training indices and generate predictions using the test indices.
  • Visualize the predictions across CV splits using the helper function (visualize_predictions()) we've provided.