{"uid":"cap_ZmBNT1yTLtgAZajfyPqHa","slug":"time-series-completeness-string-normalisation-category-frequencies-1747ba00","name":"Time Series Completeness — String Normalisation + Category Frequencies","description":"Time series completeness — String normalisation + Category frequencies: String normalisation + Category frequencies 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-16-024?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_gbEWSZWSYgdu3SXPV3eHz","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 time series completeness records (observed vs expected data points) by running two local analytical reports: string normalisation and category frequency analysis, on up to 200 rows.","exampleAgentPrompt":"I have up to 200 rows of time series completeness records with row IDs, dataset groups like 'group-a' and 'group-b', dates like 2026-10-01, and observed vs expected point counts — can you run string normalisation and category frequency analysis on them and give me both reports?","exampleUseCases":[{"title":"Audit sensor data collection completeness","prompt":"I have daily sensor readings for October 2026 across two sensor groups — 'sensor-north' and 'sensor-south'. Each row has an observed count and an expected count of data points. Can you normalize the group names and give me the category frequency breakdown so I can see which groups are contributing most rows?"},{"title":"Monitor ETL pipeline data gaps","prompt":"We run an ETL pipeline that logs observed vs expected data points per dataset group each day. Here are 150 rows covering the last 5 months — can you run string normalization and category frequency analysis so I can spot which groups appear most often and whether naming is consistent?"},{"title":"QA report for ingestion completeness tracking","prompt":"I'm doing a quality audit on our data ingestion logs. I have records with row IDs, group labels, dates, and observed vs expected point counts for up to 180 entries. Please run the string normalisation and category frequencies reports so I can clean up group naming and understand the distribution across categories."}],"resultDescription":"Returns two analytical reports for the submitted rows: (1) a string normalisation report that standardises the dataset_group and other string fields, and (2) a category frequency report showing how often each dataset group or category appears across the submitted records. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Rows array empty or missing — validation error returned","More than 200 rows submitted — rejected with max items error","Invalid date format (not YYYY-MM-DD) — schema validation failure","observed_points or expected_points out of allowed numeric range — rejected","Missing required fields (row_id, date, observed_points, expected_points) — schema error","Payment of 0.01 USDC not received — 402 Payment Required response"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously clean up string/category naming inconsistencies AND understand the frequency distribution of dataset groups in time series completeness data (observed vs expected point counts). It is ideal for data quality audits on small-to-medium batches (up to 200 rows) where you want two complementary reports in a single low-cost call. Prefer alternatives if you need prediction, live data lookup, deduplication, or statistical distribution reports beyond category frequencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:14.522Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-string-normalisation-category-frequencies-1747ba00"}