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.
Cet exercice fait partie du cours
Anomaly Detection in Python
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Create three new features from the DatetimeIndex
apple['day_of_week'] = ____
apple['month'] = ____
apple['day_of_month'] = _____