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.
Deze oefening maakt deel uit van de cursus
Anomaly Detection in Python
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Create three new features from the DatetimeIndex
apple['day_of_week'] = ____
apple['month'] = ____
apple['day_of_month'] = _____