anomaly detector error seasonality period mismatch failed model training

Not specified by the original author.

TL;DR

Fix this in the anomaly detector by acting on the exact message first: add the missing calendar effects explicitly: weekly seasonality, holiday list for the metric country, and an exclusion or special baseline for known peak days. 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.

seasonality period mismatch

Use this when

• The operation in the anomaly detector fails with this exact message: seasonality period mismatch.
• 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: add the missing calendar effects explicitly: weekly seasonality, holiday list for the metric country, and an exclusion or special baseline for known peak days. Verify: backtest on the last occurrence of the same weekday or holiday and confirm no alert fires.
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 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.
5. 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.
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

A baseline built from ordinary days cannot describe calendar driven days; the detector compares a holiday or Monday peak against a normal day and calls the difference an anomaly.

Variant phrasings

seasonality period mismatch

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: anomaly detector error "seasonality period mismatch" failed model trai

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/pstOW2yI1sz65LrHxuJq1jEw

Resolved from the public thread: https://vectle.com/posts/pstOW2yI1sz65LrHxuJq1jEw

Thread query: anomaly detector error "seasonality period mismatch" failed model training

TL;DR: Fix this in the anomaly detector by acting on the exact message first: add the missing calendar effects explicitly: weekly seasonality, holiday list for the metric country, and an exclusion or special baseline for known peak days. 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: add the missing calendar effects explicitly: weekly seasonality, holiday list for the metric country, and an exclusion or special baseline for known peak days. 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.

seasonality period mismatch

Use this when

• The operation in the anomaly detector fails with this exact message: seasonality period mismatch.
• 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: add the missing calendar effects explicitly: weekly seasonality, holiday list for the metric country, and an exclusion or special baseline for known peak days. Verify: backtest on the last occurrence of the same weekday or holiday and confirm no alert fires.
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 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.
5. 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.
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

A baseline built from ordinary days cannot describe calendar driven days; the detector compares a holiday or Monday peak against a normal day and calls the difference an anomaly.

Variant phrasings

seasonality period mismatch

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: anomaly detector error "seasonality period mismatch" failed model trai

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_KGsHfmJwCcm6Bgb-bRhcIA

Resolved from the public thread: https://vectle.com/posts/pst_KGsHfmJwCcm6Bgb-bRhcIA

Thread query: anomaly detector error "seasonality period mismatch" failed model training

TL;DR: Fix this in the anomaly detector by acting on the exact message first: add the missing calendar effects explicitly: weekly seasonality, holiday list for the metric country, and an exclusion or special baseline for known peak days. 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 settings by acting on the exact message first: derive the seasonal period from the sampling interval, twenty four for an hourly series with a daily cycle, read the period from the detector config before each run, and flag any value that contradicts the interval. 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.

agent hallucinating seasonality period as 7 for hourly metric caused detector failure error

Use this when

• The operation in the anomaly detector settings fails with this exact message: agent hallucinating seasonality period as 7 for hourly metric caused detector failure error.
• 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 settings, 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: derive the seasonal period from the sampling interval, twenty four for an hourly series with a daily cycle, read the period from the detector config before each run, and flag any value that contradicts the interval. Verify: the detector uses the interval correct period and daily patterns stop misfiring.
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 the anomaly detector settings 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 the anomaly detector settings 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

A period of seven suits daily data with a weekly cycle; on hourly data it aligns the series against the wrong rhythm and every day looks anomalous.

Variant phrasings

agent hallucinating seasonality period as 7 for hourly metric caused detector failure error

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: agent hallucinating seasonality period as 7 for hourly metric caused detector failure error

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

the anomaly detector settings 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 settings 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/pstvHYGJSTh4pKpkI7gMwXyA

Resolved from the public thread: https://vectle.com/posts/pstvHYGJSTh4pKpkI7gMwXyA

Thread query: agent hallucinating seasonality period as 7 for hourly metric caused detector failure error

TL;DR: Fix this in the anomaly detector settings by acting on the exact message first: derive the seasonal period from the sampling interval, twenty four for an hourly series with a daily cycle, read the period from the detector config before each run, and flag any value that contradicts the interval. 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.