{"uid":"cap_hNJJAG_4Q_TCgj1pCVit6","slug":"time-series-completeness-category-frequencies-adjacent-row-changes-ec8783b5","name":"Time Series Completeness — Category Frequencies & Adjacent Row Changes","description":"Time series completeness — Category frequencies + Adjacent row changes: Category frequencies + Adjacent row changes 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-16-104?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_QsBhE_s0H8yZh1V3Q9zuP","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 data by computing category frequencies and adjacent row-to-row changes for up to 200 rows, returning two local analytical reports per call.","exampleAgentPrompt":"I have daily completeness records for two dataset groups — group-a and group-b — with observed and expected data points from October 2026. Can you run a category frequencies and adjacent row changes analysis on those rows using observed_points as the field?","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I have 30 days of completeness records for my data pipeline — each row has a date, a dataset group like 'pipeline-etl' or 'pipeline-raw', and the observed vs expected data points. Can you compute the category frequencies across groups and flag where adjacent rows show big swings in observed_points?"},{"title":"Multi-group sensor data gap detection","prompt":"My IoT sensor logs have observed and expected reading counts per day for three sensor groups: 'temp', 'humidity', and 'pressure'. Run a frequencies and adjacent-change analysis on my 90 rows so I can see which groups are most common and where the day-over-day observed_points change the most."},{"title":"Weekly completeness trend review","prompt":"I'm tracking expected vs observed data points for two reporting groups — 'finance' and 'ops' — across 52 weeks. Please analyze these 104 rows for category frequency distribution and consecutive row changes in expected_points so I can spot any irregularities in my completeness trend."}],"resultDescription":"Returns two analytical reports for the submitted rows: (1) a category frequency breakdown showing how often each dataset_group appears, and (2) an adjacent row change report showing the delta in the selected field (observed_points or expected_points) between consecutive rows — all computed locally without any external lookup or prediction.","failureModes":["More than 200 rows submitted — request will be rejected due to maxItems constraint","Missing required fields (row_id, date, observed_points, expected_points) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Numeric values outside allowed range (-1e12 to 1e12) are rejected","Empty rows array fails minItems:1 constraint"],"whenToPreferThis":"Choose this endpoint when you have structured time series completeness records (observed vs expected points by group and date) and need both a frequency distribution of categories and a row-by-row delta analysis in a single low-cost call. It is best for offline/batch quality checks on up to 200 rows where no external data enrichment, prediction, or live lookup is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:33:51.258Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-category-frequencies-adjacent-row-changes-ec8783b5"}