What is outlier detection in time series data?

What is outlier detection in time series data?

Outlier detection in time series data involves identifying data points that deviate significantly from the expected pattern.

Why is it important to detect outliers in time series?

Detecting outliers is crucial as they can indicate anomalies, errors, or significant events that may affect analysis.

What methods are commonly used for outlier detection?

Common methods include statistical tests, machine learning algorithms, and visualization techniques like box plots.

Can you give an example of an outlier in a time series?

Sure! An example could be a sudden spike in sales data during a holiday season, which is unusual compared to other periods.

How can I implement outlier detection in Python?

You can use libraries like Pandas and Scikit-learn to implement outlier detection using methods like Isolation Forest or Z-score.