{"uid":"cap_4ebHPdP5_zQa3x4qkF272","slug":"aggregation-checks-trailing-mean-series-explicit-missing-value-fill-4eb90dcb","name":"Aggregation Checks — Trailing Mean Series + Explicit Missing-Value Fill","description":"Aggregation checks — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill for supplied aggregation checks records (source_sum, aggregate_sum). 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-17-154?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_sum","aggregate_sum"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_sum":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"aggregate_sum":{"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","source_sum":10,"aggregate_sum":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_sum":20,"aggregate_sum":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_sum":15,"aggregate_sum":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_sum":30,"aggregate_sum":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["source_sum","aggregate_sum"],"type":"string","default":"source_sum"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"},"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_yj6Ax5o5-uVPKUFyXgSle","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 a trailing mean time series and fills missing values in aggregation check records (source_sum, aggregate_sum) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"I have 150 rows of aggregation check data with dates and source_sum values — some source_sum entries are null. Can you compute a trailing mean series with a window of 7 days and fill the missing values using the mean method?","exampleUseCases":[{"title":"Smooth noisy aggregation sums for reporting","prompt":"I've got a dataset of daily aggregation records across two groups — some aggregate_sum values are missing. Run a trailing mean with window 5 and fill the gaps using median so I can see a clean trend line."},{"title":"Validate source totals against aggregate sums","prompt":"Here are 80 rows of source_sum and aggregate_sum records by date and dataset group. Apply a trailing mean series with window 3 and fill any missing source_sum values with zero so I can check for aggregation discrepancies."},{"title":"Prepare time series data for downstream reconciliation","prompt":"I need to clean up these 120 aggregation check rows before reconciliation — can you compute a rolling mean over a 10-day window on the aggregate_sum field and use mean imputation for any nulls?"}],"resultDescription":"Returns two analytical reports per call: a trailing mean series (rolling window average over the specified field across sorted date rows) and an explicit missing-value fill report (nulls replaced using the chosen method — zero, mean, or median). Reports cover up to 200 input rows grouped by dataset_group and date.","failureModes":["Fewer than 1 row submitted — validation error","More than 200 rows submitted — rejected with max-items error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Window size outside 2–200 range — rejected","source_sum or aggregate_sum values outside ±1,000,000,000,000 — rejected","Unknown fill method or field enum value — schema validation error","Payment not included or insufficient — x402 payment required response"],"whenToPreferThis":"Choose this endpoint when you need both trailing mean smoothing and missing-value imputation applied to aggregation check records (source_sum / aggregate_sum) in a single $0.01 call covering up to 200 rows. It is especially useful for data quality pipelines that need to reconcile source and aggregate totals before reporting or downstream processing, without requiring any external data lookup or live computation infrastructure.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:28.359Z","isFirstParty":false,"canonicalSlug":"aggregation-checks-trailing-mean-series-explicit-missing-value-fill-4eb90dcb"}