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How do I deal with missing data in a time series?
First work out why data is missing. A zero from a stock-out or a closed shop is different from a failed data feed. Then fill gaps using sensible methods such as carrying forward, interpolating or using same-period averages, or mark them and use a model that tolerates missing values. Document what you did.
Distinguish true zeros (no sales happened) from missing values (we do not know). Treating missing as zero can seriously understate demand.
Options:
- Interpolate short gaps in smooth series.
- Use seasonal averages for seasonal ones.
- Exclude long gaps from training.
- Fix the upstream cause so it stops recurring.
Add data completeness checks to your pipeline so gaps are caught when they happen, not months later.
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