statsmodels anomaly detection fails error "ValueError: endog must be strictly positive"

TL;DR

Fix this in statsmodels by acting on the exact message first: shift the series up by a constant so its minimum is above zero, or fit on the raw levels instead of a log transform, keeping the shift recorded so forecasts can be shifted back. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

ValueError: endog must be strictly positive

Use this when

• The operation in statsmodels fails with this exact message: ValueError: endog must be strictly positive.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in statsmodels, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: shift the series up by a constant so its minimum is above zero, or fit on the raw levels instead of a log transform, keeping the shift recorded so forecasts can be shifted back. Verify: the model fits and the fitted values map back to the original scale.
3. Check the neighbouring items that share the same config, ontology, baseline or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in statsmodels on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, decisions or alerts against the source in statsmodels so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Models fitted on log transformed data require positive inputs; zeros or negative values from returns or corrections break the transform.

Variant phrasings

ValueError: endog must be strictly positive

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: statsmodels anomaly detection fails error "ValueError: endog must be strictly positive"

The how-to phrasing for the same failure in statsmodels. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

statsmodels troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: statsmodels at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_zXkJ1SrjkK7sDUOCKrtpIg