{"uid":"cap_5hsbv5Vv2wufRZDUC5Ehv","slug":"time-series-completeness-analyzer-numeric-distribution-missing-value-d1c5c493","name":"Time Series Completeness Analyzer — Numeric Distribution + Missing-Value Fill","description":"Time series completeness — Numeric distribution + Explicit missing-value fill: Numeric distribution + Explicit missing-value fill 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-16-085?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_g5__gk-J9B1JEQa99ZhyA","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 a numeric distribution report and fills explicit missing values (zero, mean, or median) for up to 200 time series completeness records with observed and expected point counts.","exampleAgentPrompt":"I have up to 200 rows of time series completeness records with observed_points and expected_points per date and dataset group — can you run a numeric distribution analysis and fill any missing observed_points values using the median method?","exampleUseCases":[{"title":"Dataset gap audit across groups","prompt":"I have daily completeness records for two dataset groups from October 2026 — each row has observed_points and expected_points. Can you analyze the numeric distribution of observed_points and fill any missing values using the mean, for up to 50 rows?"},{"title":"Median imputation for IoT sensor logs","prompt":"My IoT sensor log has about 120 rows of time series data with dates, observed_points, and expected_points. Some observed_points are null — please fill those using median imputation and give me a numeric distribution report."},{"title":"Data pipeline completeness monitoring","prompt":"We track daily pipeline completeness across multiple groups; I have 180 rows with observed and expected point counts. Run a distribution analysis on expected_points and fill any missing expected_points values with zero so I can spot gaps."}],"resultDescription":"Returns two analytical reports: (1) a numeric distribution summary (e.g. min, max, mean, percentiles, histogram buckets) for the chosen field across the supplied rows, and (2) a table of the same rows with explicit missing values in the chosen field replaced by the selected fill strategy (zero, mean, or median). No external data is fetched; all computation is local.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — request rejected","Invalid date format (not YYYY-MM-DD) — validation error","Unknown fill method enum value — validation error","All values in chosen field are null — distribution report may be empty or undefined","row_id missing or exceeds 80 characters — schema validation failure"],"whenToPreferThis":"Choose this endpoint when you need both a statistical distribution summary and missing-value imputation for a time series completeness dataset (observed vs expected points) in a single $0.01 call, without requiring any external lookups or live data. It is ideal for batch data quality checks on up to 200 rows where you want offline, reproducible analytics using zero, mean, or median fill strategies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:00.374Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-analyzer-numeric-distribution-missing-value-d1c5c493"}