{"uid":"cap_fPAl9qvFAbUn2bKZos58w","slug":"time-series-completeness-numeric-interval-filter-explicit-missing-4fa5b861","name":"Time Series Completeness — Numeric Interval Filter + Explicit Missing-Value Fill","description":"Time series completeness — Numeric interval filter + Explicit missing-value fill: Numeric interval filter + 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-16-135?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_76We1oS_aWMzjx4JaQYS4","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":"Filters time series completeness records by a numeric interval and fills missing values using zero, mean, or median imputation — returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you filter my time series completeness records so that only rows where observed_points falls between 0 and 100 are kept, fill any missing observed_points using median imputation, and give me two analytical reports? I have up to 50 rows with fields like row_id, dataset_group, date, observed_points, and expected_points.","exampleUseCases":[{"title":"Cleaning sensor coverage gaps","prompt":"I have daily sensor coverage data for two device groups — each row has a date, observed_points, and expected_points. Can you filter out rows where observed_points is outside 5 to 500, then fill any null observed_points with the median, and give me two summary reports for up to 80 rows?"},{"title":"Validating pipeline completeness by date","prompt":"My data pipeline logs show completeness records per day — I need to keep only records where expected_points is between 10 and 1000, fill any missing expected_points with the mean, and run two analytical reports across my 120 rows grouped by dataset_group."},{"title":"Imputing missing points in blockchain data","prompt":"I've got 150 rows of time series completeness records for two blockchain datasets, and some observed_points values are null. Can you filter to observed_points between 0 and 200, use zero-fill for the missing ones, and produce two reports so I can see where coverage is weakest?"}],"resultDescription":"The endpoint returns two independent analytical reports computed from the filtered and imputed dataset. Each report reflects completeness metrics (observed vs. expected points) after applying the numeric interval filter to exclude out-of-range rows and replacing null values in the target field using the chosen fill method (zero, mean, or median). Results are based entirely on the supplied data — no external lookups or live data.","failureModes":["Input array is empty or exceeds 200 rows — validation error returned","date field does not match YYYY-MM-DD pattern — rejected with schema error","minimum value is greater than maximum — may produce empty filtered result with no error","observed_points or expected_points exceeds allowed numeric range — schema rejection","missing required fields (row_id, date) — validation failure","invalid fill method or field enum value — schema rejection"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously clean a time series completeness dataset by numeric range filtering AND impute missing values in a single call, receiving two analytical reports as output. It is ideal for batch offline analysis of up to 200 rows of observed/expected point records where no live data lookup, prediction, or external enrichment is needed. Prefer it over general-purpose analytics when your use case specifically involves completeness metrics (observed vs. expected points) with null-handling requirements.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:05.990Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-interval-filter-explicit-missing-4fa5b861"}