{"uid":"cap__bfj3CQi3s15VWwIQYJP6","slug":"time-series-completeness-numeric-row-ordering-base-100-series-8b90bfbf","name":"Time Series Completeness — Numeric Row Ordering + Base-100 Series","description":"Time series completeness — Numeric row ordering + Base-100 series: Numeric row ordering + Base-100 series 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-16-117?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"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_XTBUSTcL8wMVygK5eDno4","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 numeric row ordering and base-100 indexed series for up to 200 time series completeness records (observed vs expected points), returning two independent analytical reports.","exampleAgentPrompt":"I have daily completeness records for two dataset groups from Oct 1–4 2026 — each row has observed and expected point counts. Can you rank those rows numerically and also produce a base-100 index series anchored at row 0, using the observed_points field?","exampleUseCases":[{"title":"Pipeline completeness monitoring report","prompt":"I'm tracking data pipeline completeness across two groups for the past week — each day has observed and expected point counts. Can you rank all the rows by observed_points and give me a base-100 index series starting from row 0 so I can see relative completeness trends?"},{"title":"Dataset group benchmarking with base index","prompt":"I have 30 rows of completeness data across multiple dataset groups with observed and expected points per date. Please produce a numeric row ordering and a base-100 series on expected_points anchored at row 5 so I can benchmark each group against that baseline."},{"title":"Anomaly detection prep via completeness indexing","prompt":"Before running anomaly detection, I need to normalize my 120-row time series completeness dataset. Can you give me numeric row ranks and a base-100 index on observed_points starting from row 0 so I can spot where completeness drops below baseline?"}],"resultDescription":"Returns two independent reports: (1) a numeric row ordering ranking each input row by the selected field value, and (2) a base-100 indexed series anchored at the specified base_index row — all computed locally from the supplied rows without any external data lookups.","failureModes":["More than 200 rows submitted — exceeds maxItems limit","Missing required fields (row_id, date, observed_points, expected_points) on any row","Invalid date format (must be YYYY-MM-DD)","base_index out of range for the number of supplied rows","Null values for both observed_points and expected_points preventing computation","Field enum value not in ['observed_points','expected_points']"],"whenToPreferThis":"Choose this endpoint when you have up to 200 time series completeness records with observed and expected point counts and need both a numeric rank ordering and a base-100 normalized index series in a single call. It is ideal for offline/batch analytical prep steps where no live data lookup, external enrichment, or prediction is needed — just deterministic ranking and indexing of supplied data at low cost ($0.01 per call for two reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:46.212Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-row-ordering-base-100-series-8b90bfbf"}