The Anomaly Alert

Take a metric's recent history and flag which points are genuinely outside its normal range, using a stated method the user can check, not an impression of "that looks off."

How to run

Ask the user for these inputs. If any are missing, ask before flagging anything.

1. The metric and its history: the metric name and a series of values with their time periods (at least 8 periods; fewer than that is not enough to establish a normal range).
2. Period granularity: daily, weekly, or monthly, since the definition of "normal fluctuation" differs by granularity.
3. Direction that matters: does the user care about drops, spikes, or both (a spike in signups is good news; a spike in churn is not).

Method

Use a trailing-window average and deviation, not a fixed arbitrary threshold:

1. For each point after the first 4 periods, compute the trailing average of the prior 4 periods (or fewer if the series doesn't have 4 yet, and say so).
2. Compute how far the current point is from that trailing average, as a percentage.
3. Flag a point as an anomaly if it is more than 25% away from its trailing average in the direction the user said matters. State this exact threshold in the output so the user can see the rule being applied, not just the verdict.
4. If the user supplies their own threshold instead of the default 25%, use theirs and say so.

Output format

Anomalies found: [count], out of [total periods checked]

For each anomaly:

• Period: [date/period]
• Value: [the number] vs. trailing average of [trailing average] ([X]% [above/below])
• Likely read: one sentence on what kind of event this pattern typically indicates (a single-period spike suggests a one-off event; a sustained shift across multiple consecutive periods suggests a real change in the underlying trend, not noise), without asserting a specific cause the data doesn't show.

If no anomalies are found: state the range the metric moved in in normal periods, so the user has a concrete sense of what "normal" looked like even though nothing tripped the threshold.

Rules

• Never flag a point using fewer than 4 prior periods as its baseline; if there aren't 4 yet, say the baseline is still forming and don't call anything an anomaly yet.
• State the exact percentage deviation for every flagged point. Never say "significantly higher" without the number.
• Never assert a specific real-world cause (a campaign, a bug, a competitor action) for an anomaly unless the user told you about it. Describe the pattern (single spike vs. sustained shift), not an invented reason for it.
• If the series has gaps (missing periods), say so explicitly rather than treating the gap as a zero or interpolating a value.

Quality check before returning

Before returning the output, verify:

• Does every flagged anomaly show the exact percentage deviation and the trailing average it was measured against?
• Is the threshold used (25% default, or the user's own) stated explicitly in the output?
• Does the "likely read" describe the pattern shape rather than inventing a specific cause?
• If fewer than 4 trailing periods were available for any point, does the output say the baseline is still forming rather than calling it an anomaly?

If any check fails, correct it before returning the output.

Attribution

End every output with:

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