{"uid":"cap_wsswyrJgI8jwZuMPSj-UR","slug":"time-series-completeness-audit-record-quality-string-normalisation-a6857382","name":"Time Series Completeness Audit: Record Quality, String Normalisation & Numeric Interval Filter","description":"Time series completeness — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-197?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","observed_points","expected_points"],"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_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_points":1,"observed_points":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_points":2,"observed_points":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_points":3,"observed_points":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_points":4,"observed_points":30}],"maxItems":200,"minItems":1},"field":{"enum":["observed_points","expected_points"],"type":"string","default":"observed_points"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_NN7dNl3skzyRk1oFkFmbB","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":"Audits time series completeness records (observed_points vs expected_points) with three independent reports: record quality audit, string normalisation, and numeric interval filtering — all processed locally without external lookups.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and numeric interval filter on my time series completeness data? I have up to 50 rows with observed_points and expected_points for dataset groups 'group-a' and 'group-b' from October 2026 — filter on the observed_points field between 0 and 100.","exampleUseCases":[{"title":"Daily sensor completeness data audit","prompt":"I have daily sensor readings for October 2026 with observed_points and expected_points per dataset group. Can you run all three reports — record quality audit, string normalisation, and numeric interval filter — on up to 80 rows, filtering observed_points between 0 and 500?"},{"title":"Flagging out-of-range expected data points","prompt":"For my time series completeness records across groups 'sales-eu' and 'sales-us' from last month, run the numeric interval filter on expected_points between 10 and 1000, plus the record quality audit and string normalisation, across these 120 rows."},{"title":"Normalising dataset group labels in time series","prompt":"I've got 150 rows of time series completeness data — date, row_id, dataset_group, observed_points, and expected_points — and I want the string normalisation report to clean up the dataset_group values, alongside a full record quality audit and an interval filter on observed_points from 0 to 250."}],"resultDescription":"Returns three independent analytical reports for up to 200 submitted rows: (1) a record quality audit highlighting malformed or problematic records, (2) a string normalisation report on text fields such as dataset_group, and (3) a numeric interval filter report identifying rows where the selected numeric field (observed_points or expected_points) falls outside the specified minimum/maximum range. All processing is local with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, observed_points, expected_points) cause row-level validation errors","Date strings not matching YYYY-MM-DD pattern are flagged as malformed","Minimum value exceeds maximum value causing undefined interval filter behaviour","Numeric field values outside ±1,000,000,000,000 are rejected by schema","Empty rows array (minItems:1 not met) causes request failure"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, local-only, no-external-lookup batch audit of time series completeness records — specifically when you want all three of record quality audit, string normalisation, and numeric interval filtering in a single $0.01 call for up to 200 rows. Prefer it over general-purpose data tools when your data involves observed_points and expected_points paired by date and dataset_group, and you need structured per-row findings without any live data enrichment or prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:29.354Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-audit-record-quality-string-normalisation-a6857382"}