{"uid":"cap_XY6wJ2XyuozUfMplGg1Ok","slug":"time-series-completeness-string-normalisation-two-measure-correlation-aa570b61","name":"Time Series Completeness: String Normalisation + Two-Measure Correlation","description":"Time series completeness — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied time series completeness records (observed_points, expected_points). 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-16-033?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}}},"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_9vGiyyf5EXe05nuHr1Z6y","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":"Analyzes up to 200 rows of time series completeness data (observed vs expected points) using string normalisation and two-measure correlation, producing 2 independent analytical reports.","exampleAgentPrompt":"Can you analyze the completeness of my time series data — I have up to 200 rows each with a date, dataset group, observed points, and expected points — and give me both a string normalisation report and a two-measure correlation report showing how well observed tracks expected?","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I have 150 rows of daily pipeline metrics from October 2026, each tagged with a dataset group, and I want to know how well my observed data points match expected counts — can you run string normalisation and a two-measure correlation analysis on them?"},{"title":"Cross-group data gap detection","prompt":"We have two dataset groups — 'group-a' and 'group-b' — with about 90 rows of observed vs expected point counts recorded by date. Can you run a completeness correlation analysis to see if either group is consistently under-delivering data?"},{"title":"Monthly ingestion quality review","prompt":"Here are 60 rows of time series completeness records for our ingestion pipeline, with dates, observed point counts, and expected point counts per group. Please run both the string normalisation and two-measure correlation reports so I can share them with the data engineering team."}],"resultDescription":"Returns 2 independent analytical reports for the supplied time series completeness rows: (1) a string normalisation report and (2) a two-measure correlation report comparing observed_points to expected_points across the provided dates and dataset groups. No external lookups or predictions are made — all analysis is performed on the supplied data only.","failureModes":["More than 200 rows submitted — exceeds maxItems limit","Missing required fields (row_id, date, observed_points, expected_points) cause validation errors","Malformed date strings not matching YYYY-MM-DD pattern rejected","Numeric values outside allowed range (-1e12 to 1e12) rejected","Empty rows array (minItems:1 not met) causes error","Payment not processed — x402 payment of 0.01 USDC required per call"],"whenToPreferThis":"Choose this endpoint when you need offline, supply-side statistical analysis of time series completeness data (observed vs expected point counts) without any external data lookups or predictions. It is ideal for data quality audits, pipeline monitoring, and completeness reporting where you already have the raw rows and need two complementary analytical views (string normalisation + correlation) in a single low-cost call. Prefer this over general-purpose analytics tools when you need a structured, repeatable completeness report on up to 200 date-grouped rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:18.291Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-string-normalisation-two-measure-correlation-aa570b61"}