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.
Este exercício faz parte do curso
Anomaly Detection in Python
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Create three new features from the DatetimeIndex
apple['day_of_week'] = ____
apple['month'] = ____
apple['day_of_month'] = _____