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Precision

As explained in Time Series Forecasting Using Foundation Models

Precision is the proportion of observations labeled positive by a detection system that are actually positive.

Precision measures how many observations labeled positive by a detection system are actually positive. For anomaly detection, it is the share of detected anomalies that are true anomalies. section 3.8.2

Compute precision as true positives divided by all predicted positives. Unlike recall, it penalizes false positives: labeling more points as anomalous can increase coverage while making detections less trustworthy. section 3.8.2

Anomaly-detection evaluation

A detector with precision 0.38 correctly identifies only 38% of its anomaly alerts. Chronos reaches 100% recall but 21% precision, so most of its alerts are false positives. section 3.8.2 section 5.8

How Time Series Forecasting Using Foundation Models covers it

How deep it goes
Defines and implements the metric, then demonstrates its trade-off with recall when prediction intervals change.
Examples
Uses TimeGPT, Chronos, Llama-3.2, and Time-LLM anomaly detections on daily New York City taxi data.
What it assumes
Assumes predicted and actual positive labels, while the evaluation function supplies the confusion-count calculation.
What you can do after
Diagnose false-positive-heavy detectors and choose larger prediction intervals when avoiding false positives matters.
How it connects
Connects precision with recall, F1 score, anomaly detection, prediction intervals, and false positives.
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