{"uid":"cap_G0Qr5U_8Zo7_Sj4yoxv_r","slug":"feature-availability-analytics-trailing-mean-series-adjacent-row-7f902869","name":"Feature Availability Analytics: Trailing Mean Series + Adjacent Row Changes","description":"Feature availability — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes for supplied feature availability records (populated_features, expected_features). 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-10-146?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1},"field":{"enum":["populated_features","expected_features"],"type":"string","default":"populated_features"},"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_Yh3t9heu2eUUk0rTEp_rx","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 adjacent row change reports for feature availability data (populated_features vs expected_features) over up to 200 rows.","exampleAgentPrompt":"Analyze my feature availability records — I have 4 rows across group-a and group-b from 2026-10-01 to 2026-10-04 with populated_features and expected_features values — and give me both a trailing mean series (window size 2) and the adjacent row changes for populated_features.","exampleUseCases":[{"title":"MLOps feature store audit","prompt":"I have 100 daily feature availability records across multiple dataset groups — can you compute a trailing mean series with window size 7 and adjacent row changes for populated_features so I can spot degradation trends?"},{"title":"Expected vs populated feature drift check","prompt":"Run a trailing mean analysis with window 3 on these 50 feature availability records using expected_features as the field, and show me adjacent row changes so I can see where expectations diverged from reality."},{"title":"Dataset group completeness monitoring","prompt":"I need to monitor feature completeness over the last 30 daily snapshots. Can you compute both the rolling mean (window of 5) and the adjacent differences for populated_features across my dataset groups?"}],"resultDescription":"Returns two reports derived from the supplied feature availability rows: (1) a trailing mean time-series showing windowed averages of the selected field over the specified window, and (2) an adjacent row changes report showing the delta between consecutive rows — all computed locally with no external lookups.","failureModes":["Fewer than 1 or more than 200 rows supplied — request rejected","Invalid date format (not YYYY-MM-DD) — validation error","Window size outside 2–200 range — rejected","null values in populated_features or expected_features may affect mean/delta calculations","Unknown field value (not 'populated_features' or 'expected_features') — enum validation error","Payment failure — 402 response, no report generated"],"whenToPreferThis":"Choose this endpoint when you need both a rolling/trailing mean time-series AND adjacent row delta analysis on feature availability records in a single paid call. It is ideal for data quality monitoring, MLOps pipelines, or analytics workflows where you supply structured rows with populated and expected feature counts and want two complementary statistical views without building local analytics logic.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T06:58:38.836Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-trailing-mean-series-adjacent-row-7f902869"}