{"uid":"cap_mdybkQfNRgU0bbfsYOGX4","slug":"time-series-completeness-daily-sum-series-explicit-missing-value-fill-3f06f031","name":"Time Series Completeness — Daily Sum Series + Explicit Missing-Value Fill","description":"Time series completeness — Daily sum series + Explicit missing-value fill: Daily sum series + 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-16-145?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_IGQBvoQ7kK72VIs5_pv6c","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 daily time series completeness records and fills missing values using zero, mean, or median imputation, producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run a daily sum series and missing-value fill analysis on my time series completeness data — I have about 50 rows with observed_points and expected_points per date, grouped into two dataset groups, and I want median imputation applied to observed_points?","exampleUseCases":[{"title":"IoT sensor gap detection and fill","prompt":"I have daily IoT sensor readings for October with observed_points and expected_points for two device groups — can you run a completeness report and fill in any missing observed_points values using median imputation across those 30 rows?"},{"title":"Marketing campaign daily metrics audit","prompt":"Run a daily sum series completeness check on my campaign data — I have 60 rows across three campaign groups with observed and expected daily impressions, and I want zero-fill applied to any missing observed_points."},{"title":"Financial data series completeness review","prompt":"Can you analyze completeness for my daily financial dataset — 120 rows with observed_points and expected_points across several portfolio groups — and produce a missing-value fill report using mean imputation on the expected_points field?"}],"resultDescription":"Returns two analytical reports: (1) a daily sum series report summarizing observed vs expected points per date and group, and (2) an explicit missing-value fill report where null or absent values in the chosen field (observed_points or expected_points) are replaced using the selected method (zero, mean, or median), covering up to 200 input rows.","failureModes":["Input array exceeds 200 rows — request will be rejected","Invalid date format (must match YYYY-MM-DD pattern)","Missing required fields (row_id, date, observed_points, expected_points) cause row-level errors","Invalid fill method or field enum value causes a validation error","Payment of 0.01 USDC not completed — endpoint returns 402 and declines to process"],"whenToPreferThis":"Choose this endpoint when you need both a daily sum series completeness summary AND a missing-value imputation pass in a single call for up to 200 rows of time series data. It is ideal for offline/batch analytical use cases where the data is already collected and no live external lookup, prediction, or real-time monitoring is required. It offers a cost-effective two-report bundle at $0.01 USDC.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:44.223Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-daily-sum-series-explicit-missing-value-fill-3f06f031"}