{"uid":"cap_ljkxidRKAkO3S1izD-8wu","slug":"dataset-partition-sizes-trailing-mean-series-iqr-outlier-flags-87fd2e84","name":"Dataset Partition Sizes — Trailing Mean Series + IQR Outlier Flags","description":"Dataset partition sizes — Trailing mean series + IQR outlier flags: Trailing mean series + IQR outlier flags for supplied dataset partition sizes records (row_count, file_mb). 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-07-149?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","row_count","file_mb"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"file_mb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"row_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","file_mb":1,"row_count":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","file_mb":2,"row_count":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","file_mb":3,"row_count":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","file_mb":4,"row_count":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["row_count","file_mb"],"type":"string","default":"row_count"},"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_jz4h-oAGy3gGMVstFje8q","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 moving averages and IQR-based outlier flags for dataset partition size metrics (row_count, file_mb) across up to 200 supplied records.","exampleAgentPrompt":"Analyze these 50 dataset partition records for trailing mean trends and IQR outlier flags on the row_count field, using a window of 7 — I want to know which partitions are statistical anomalies.","exampleUseCases":[{"title":"Pipeline partition spike detection","prompt":"I have 120 daily dataset partition records with row counts and file sizes — can you compute a 14-day trailing mean and flag any IQR outliers in the row_count field so I can see which days had abnormal partition sizes?"},{"title":"File size trend monitoring across groups","prompt":"Here are 80 partition size records split across three dataset groups. Run a trailing mean with a window of 5 and flag file_mb outliers using IQR so I can identify which groups have erratic file sizes."},{"title":"Data quality audit before ETL promotion","prompt":"Before I promote this dataset to production, analyze these 200 partition records — compute a trailing mean series and mark any row_count values that fall outside the IQR range as outliers, using a window of 10."}],"resultDescription":"Returns two analytical reports covering: (1) a trailing mean time series for the selected metric (row_count or file_mb) across the supplied records using the specified window size, and (2) IQR-based outlier flags identifying which records deviate significantly from the interquartile range. Results are computed locally with no external lookups.","failureModes":["Fewer than 1 row submitted — minimum 1 row required","More than 200 rows submitted — payload exceeds maxItems limit","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Window size larger than number of rows may produce sparse or no trailing means","null values in row_count or file_mb may reduce effective sample for IQR computation","Missing required fields (row_id, dataset_group, date) cause request rejection"],"whenToPreferThis":"Choose this endpoint when you have up to 200 dataset partition size records and need both a trailing moving average series and IQR-based outlier detection in a single call without any external data lookups. It is well-suited for data engineers auditing ETL pipeline health, detecting partition size spikes, or performing pre-promotion quality checks on batch dataset records. Prefer this over generic statistics APIs when you need the combined trailing-mean + IQR outlier report format specifically for row_count or file_mb metrics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:35.198Z","isFirstParty":false,"canonicalSlug":"dataset-partition-sizes-trailing-mean-series-iqr-outlier-flags-87fd2e84"}