Ongoing outlier detection monitors data over the long term so a system can reliably identify relevant outliers as they eventually appear. Unlike one-off analysis, it requires a maintainable detection process rather than a single review of current records. section 4.13
A practical system typically combines several perspectives:
- Reformat records into aggregate tables to expose collective outliers, such as anomalous months with unusual departmental hiring totals. section 4.13
- Represent evolving measurements as time series to identify temporal anomaly patterns that unusual individual rows may conceal. section 4.13
- Use a broader and more diverse detector set than a one-off investigation may require, then have experts review flagged cases. section 4.13
- Log reviewed outliers and tune scores over time so important anomaly types rank higher and unwanted flags rank lower. section 4.13
