How can you identify and handle outliers in time series data?

Instruction: Describe methods for identifying outliers in time series data and strategies for dealing with them.

Context: This question assesses the candidate's ability to effectively identify and mitigate the impact of outliers, ensuring the integrity of time series analysis.

Updated

Example Answer

I would flag unusual values relative to the expected pattern at that time, not just against the overall mean. A weekend peak can be normal even if it looks extreme in a global boxplot. Residuals from a seasonal baseline or a robust local estimate can help identify cases to investigate.

Then I would check the source and event context. A duplicate upload may need correction; a real campaign spike may belong in the data with an event indicator. I would retain an audit trail and compare the forecast with and without the proposed treatment. For live forecasting, the detection rule must use information available at the time. I would not remove a value solely because doing so improves the training fit.

Make it your own

Use one actual anomaly, how you verified it, and why you retained or changed it.

Why this works

It treats an outlier flag as a starting point for judgment.

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