{"uid":"cap_ZqRtGtGHbhQRIy-iM0raS","slug":"time-series-completeness-string-normalisation-daily-sum-series-36ba1baf","name":"Time Series Completeness — String Normalisation + Daily Sum Series","description":"Time series completeness — String normalisation + Daily sum series: String normalisation + Daily sum 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-16-027?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"}}},"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_onZ4NPs07OKOmekP_IIMA","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":"Performs string normalisation and daily sum series analysis on time series completeness records (observed_points, expected_points) for up to 200 rows, returning two independent analytical reports.","exampleAgentPrompt":"I have a batch of up to 50 daily time series completeness records with observed_points and expected_points — can you run string normalisation on the dataset group labels and compute the daily sum series for the observed_points field?","exampleUseCases":[{"title":"Data pipeline completeness audit","prompt":"I have 120 rows of daily completeness logs from my ETL pipeline — each row has a dataset_group, date, observed_points, and expected_points. Can you normalize the group name strings and give me a daily sum series for observed_points so I can see where data is missing?"},{"title":"Monitoring dashboard data prep","prompt":"For my monitoring dashboard I need to clean up and aggregate some raw completeness records — about 80 rows covering the last quarter. Can you run string normalisation on the dataset groups and produce the daily sum series for expected_points?"},{"title":"QA report on sensor data coverage","prompt":"I'm doing a QA pass on sensor coverage data — I have 40 rows with dates, group names, and counts of observed vs expected readings. Please normalize the group name strings and calculate the daily sum series for both observed_points and expected_points so I can compare them."}],"resultDescription":"Returns two independent analytical reports: (1) a string-normalised view of the dataset_group labels across all supplied rows, and (2) a daily sum series aggregating either observed_points or expected_points by date — all computed locally without external lookups or predictions, covering up to 200 input rows.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure","Null numeric values may affect sum calculations","Invalid field enum value (must be 'observed_points' or 'expected_points') — rejected","Empty rows array — minimum 1 item required"],"whenToPreferThis":"Choose this endpoint when you need two complementary analytical views — string normalisation of categorical labels and a daily aggregate sum — on a batch of time series completeness records in a single low-cost call. Ideal for data quality pipelines, ETL monitoring, and dashboard prep where you supply the data and want fast, local computation without external dependencies. Best suited for batches up to 200 rows priced at $0.01 USDC per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:43.059Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-string-normalisation-daily-sum-series-36ba1baf"}