How do you handle missing values in time series data?

Instruction: Discuss your approach to identifying, analyzing, and imputing missing values in a time series dataset. Mention any specific methods or techniques you prefer.

Context: This question assesses the candidate's ability to deal with incomplete data, which is a common issue in time series analysis. It evaluates their strategies for managing missing data, including their choice of imputation methods, reflecting their practical skills in preparing datasets for analysis.

Updated

Example Answer

I would first check why the observations are missing. A sensor outage, a closed store, and a missing transaction upload mean different things; none should automatically become zero. I would also separate reconstructing an old gap from creating a feature for a live forecast.

Interpolation or backfill can use a later observation, so they are unsuitable when that observation would not have existed at prediction time. For online use, I would consider a bounded past-only fill or a filtering model, keep a missingness flag, and record how old the last observation is. I would fit the imputation within each training window and test it on realistic gaps. Long outages may be better left uncertain than filled with confident-looking estimates.

Make it your own

Specify whether your task permits later observations, the typical gap length, and the validation you would use.

Why this works

It treats timing as part of data quality.

Follow-up to practice

How would your approach change for a week-long outage?

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