{"uid":"cap_uqYBR10IH4-8Gg8h4ljAa","slug":"time-series-completeness-adjacent-row-changes-missing-value-fill-8b1071d0","name":"Time Series Completeness — Adjacent Row Changes + Missing-Value Fill","description":"Time series completeness — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + 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-16-162?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_UzMME82kPvolLvwnXltYb","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 adjacent row differences and filling missing values (zero, mean, or median) for observed_points or expected_points fields across up to 200 rows.","exampleAgentPrompt":"Can you analyze this time series completeness dataset — up to 50 rows with date, observed_points, and expected_points per group — and give me both the adjacent row changes and fill the missing observed_points values using the median method?","exampleUseCases":[{"title":"Daily data pipeline completeness audit","prompt":"I have 90 rows of daily completeness records with dates, dataset groups, observed_points, and expected_points — can you compute the adjacent row differences and fill any missing observed_points using the mean so I can see where my pipeline dropped data?"},{"title":"Sensor coverage gap detection","prompt":"Here are 120 rows of sensor coverage data by date and group. I want to see how observed_points changed between consecutive days and fill any nulls in expected_points using zero so I can flag completeness gaps."},{"title":"Weekly reporting on data ingestion rates","prompt":"I've got 40 rows of weekly ingestion completeness records across several dataset groups. Can you run both the adjacent change analysis and a median fill on the observed_points field so I can include this in my data quality report?"}],"resultDescription":"Returns two analytical reports: (1) adjacent row changes showing differences between consecutive observed_points or expected_points values per group, and (2) a missing-value fill report where nulls in the selected field are replaced using zero, mean, or median — all computed locally from the supplied rows, with no external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, date, observed_points, expected_points) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Both observed_points and expected_points are null for all rows, making fill and change computation meaningless","Payment of 0.01 USDC not completed — endpoint returns 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you have up to 200 time series completeness records and need both adjacent-row change detection and missing-value imputation in a single call. It is best for offline, self-contained analysis of completeness metrics (observed vs expected points) where no external data, predictions, or live lookups are needed. Prefer it over general analytics platforms when you want a lightweight, pay-per-use computation without setting up infrastructure.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:52.176Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-adjacent-row-changes-missing-value-fill-8b1071d0"}