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Isolation Forest on time series

If you want to use all the information available, you can fit a multivariate outlier detector to the entire dataset. The multivariate approach also enables you to extract more features from time series to enhance model performance.

Practice creating new features from a DatetimeIndex and fitting an outlier detector on them using the apple dataset, which has already been loaded with a DatetimeIndex.

Also, recall the random_state parameter, which can be used to generate reproducible results.

This exercise is part of the course

Anomaly Detection in Python

View Course

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create three new features from the DatetimeIndex
apple['day_of_week'] = ____
apple['month'] = ____
apple['day_of_month'] = _____
Edit and Run Code