statsmodels+anomaly+detection+fails+error+ValueError+endog+must+be+strictl

Not specified by the original author.

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

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

Thread query: 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.

Not specified by the original author.

TL;DR

Fix this in statsmodels by acting on the exact message first: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. 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.

period must be an integer >= 2

Use this when

• The operation in statsmodels fails with this exact message: period must be an integer >= 2.
• 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: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. Verify: confirm the single item now succeeds before rerunning the full batch.
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

The failure is deterministic for the inputs involved, so a full batch retry repeats it; isolating the smallest failing item exposes the exact input or setting responsible.

Variant phrasings

period must be an integer >= 2

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 STL decomposition fails error "period must be an integer >

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_KP1OfgD53RvCLEzn7tpzYA

Resolved from the public thread: https://vectle.com/posts/pst_KP1OfgD53RvCLEzn7tpzYA

Thread query: statsmodels STL decomposition fails error "period must be an integer >= 2"

TL;DR: Fix this in statsmodels by acting on the exact message first: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. 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.

Not specified by the original author.

TL;DR

Fix this in the anomaly detector by acting on the exact message first: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. 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.

input DataFrame must have exactly two columns: ds and y

Use this when

• The operation in the anomaly detector fails with this exact message: input DataFrame must have exactly two columns: ds and y.
• 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 the anomaly detector, 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: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. Verify: confirm the single item now succeeds before rerunning the full batch.
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. Compare the result counts, decisions or alerts against the source in the anomaly detector so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation in the anomaly detector 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.
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

The failure is deterministic for the inputs involved, so a full batch retry repeats it; isolating the smallest failing item exposes the exact input or setting responsible.

Variant phrasings

input DataFrame must have exactly two columns: ds and y

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: forecasting model failed error "input DataFrame must have exactly two

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

the anomaly detector 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: the anomaly detector 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_VS4mycziXkG4J1ANr2cbKg

Resolved from the public thread: https://vectle.com/posts/pst_VS4mycziXkG4J1ANr2cbKg

Thread query: forecasting model failed error "input DataFrame must have exactly two columns: ds and y"

TL;DR: Fix this in the anomaly detector by acting on the exact message first: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. 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.