{"uid":"cap_-pMUEeo-QkwfCV27Ezns4","slug":"data-ingestion-batches-trailing-mean-series-iqr-outlier-flags-3b1b7164","name":"Data Ingestion Batches — Trailing Mean Series + IQR Outlier Flags","description":"Data ingestion batches — Trailing mean series + IQR outlier flags: Trailing mean series + IQR outlier flags for supplied data ingestion batches records (received_rows, accepted_rows). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-01-149?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","received_rows","accepted_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"accepted_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"received_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","accepted_rows":1,"dataset_group":"group-a","received_rows":10},{"date":"2026-10-02","row_id":"row-2","accepted_rows":2,"dataset_group":"group-b","received_rows":20},{"date":"2026-10-03","row_id":"row-3","accepted_rows":3,"dataset_group":"group-a","received_rows":15},{"date":"2026-10-04","row_id":"row-4","accepted_rows":4,"dataset_group":"group-b","received_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["received_rows","accepted_rows"],"type":"string","default":"received_rows"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_lljyiOS70Tn169eNVeSBq","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes trailing mean time series and IQR-based outlier flags for received_rows and accepted_rows fields in data ingestion batch records (up to 200 rows, 2 reports per call).","exampleAgentPrompt":"Can you run trailing mean and IQR outlier detection on these ingestion batch records — I have 120 rows with received_rows and accepted_rows per date, targeting the received_rows field with a window of 7?","exampleUseCases":[{"title":"ETL pipeline anomaly detection","prompt":"I have 90 days of daily ingestion batch logs with received_rows and accepted_rows per dataset group — can you compute a 7-day trailing mean and flag IQR outliers on the received_rows field so I can spot problematic ingestion days?"},{"title":"Data acceptance rate quality monitoring","prompt":"Here are 60 records of our nightly ingestion batches. Run trailing mean smoothing with a window of 5 and flag any IQR outliers on the accepted_rows field — I want to see which nights had statistically unusual acceptance counts."},{"title":"Multi-group ingestion trend analysis","prompt":"I've got batch ingestion data across two dataset groups over the past 200 days. Can you compute the trailing mean series and IQR outlier flags for received_rows using a window of 14, so I can compare trend stability between the groups?"}],"resultDescription":"Returns two analytical reports: (1) a trailing mean time series computed over the specified window for the chosen field (received_rows or accepted_rows), and (2) per-row IQR-based outlier flags indicating which records fall outside the interquartile range. Both reports are derived entirely from the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — minimum 1 row required","More than 200 rows supplied — batch size limit exceeded","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) on any row","Date values not matching YYYY-MM-DD format — pattern validation failure","Window size outside 2–200 range — parameter validation error","All numeric values null — insufficient data for statistical computation","Payment not included or insufficient — x402 payment required at $0.01 USDC"],"whenToPreferThis":"Choose this endpoint when you need both a rolling/trailing mean trend and IQR outlier detection on ingestion batch metrics (received_rows or accepted_rows) in a single call, without any external data dependencies. It is purpose-built for data engineering and ETL quality use cases and provides two statistical reports per call at minimal cost, making it ideal for lightweight batch anomaly detection pipelines that operate on self-contained historical records.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T18:53:31.727Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-trailing-mean-series-iqr-outlier-flags-3b1b7164"}