Discuss the impact of outliers in time series data and how to mitigate their effects.
Instruction: Explain the potential effects of outliers on time series analysis and the strategies to handle them.
Updated
Example Answer
An unusual observation can pull a fitted level or trend, inflate estimated uncertainty, or distort seasonal patterns. Its impact depends on the model and on whether it is an isolated error, a real event, or a lasting change.
I would investigate before altering it. A duplicate transaction can be corrected at the source; a genuine campaign spike may need an event term or a separate scenario. If a robust fit is appropriate, I would compare it with the untreated baseline and inspect later forecasts. I would keep the original observation and record the treatment. Removing a value because it is inconvenient can hide exactly the event the business needs to forecast or detect.
Make it your own
Explain one outlier, its source, and the effect you would check in the fitted model.
Why this works
It connects treatment to the mechanism of failure.