{"uid":"cap_ge8LbwuhrpbB3FDMG-2pr","slug":"geospatial-tile-processing-trailing-mean-series-explicit-missing-4c37eb59","name":"Geospatial Tile Processing — Trailing Mean Series + Explicit Missing-Value Fill","description":"Geospatial tile processing — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill for supplied geospatial tile processing records (tile_count, processing_seconds). 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-weather-21-154?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","tile_count","processing_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"tile_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80},"processing_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","tile_count":10,"station_group":"group-a","processing_seconds":1},{"date":"2026-10-02","row_id":"row-2","tile_count":20,"station_group":"group-b","processing_seconds":2},{"date":"2026-10-03","row_id":"row-3","tile_count":15,"station_group":"group-a","processing_seconds":3},{"date":"2026-10-04","row_id":"row-4","tile_count":30,"station_group":"group-b","processing_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["tile_count","processing_seconds"],"type":"string","default":"tile_count"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"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_LwRp740Cu9Gi1OTBFRQ1P","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 trailing moving-average series and fills missing values in geospatial tile processing records (tile_count, processing_seconds) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"I have a dataset of up to 200 geospatial tile processing records with tile_count and processing_seconds fields — can you compute a trailing mean series using a window of 3 and fill any missing tile_count values using the median method?","exampleUseCases":[{"title":"Smooth tile throughput over time","prompt":"I have daily tile processing records for two station groups over the past month — can you run a trailing mean with a window of 7 on the tile_count field and fill any gaps using the mean method so I can see a smoothed trend?"},{"title":"Clean missing processing time data","prompt":"My geospatial pipeline logs have some null processing_seconds values mixed in — can you fill those missing entries using the median and also give me a trailing mean series with a window of 5 so I can spot anomalies?"},{"title":"Audit tile counts by station group","prompt":"I've got 150 rows of tile processing records grouped by station — run a trailing mean series with window size 4 on tile_count and fill any missing values with zero so I can compare throughput across groups."}],"resultDescription":"Returns two analytical reports: (1) a trailing mean series over the chosen field (tile_count or processing_seconds) using the specified window size, and (2) the same dataset with missing values explicitly filled using the chosen method (zero, mean, or median). Both reports cover up to 200 input rows and are computed locally with no external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Invalid date format (non-YYYY-MM-DD) — validation error","Missing required fields (row_id, date, tile_count, processing_seconds) — schema error","Window size outside 2–200 range — parameter error","Numeric values outside allowed bounds — validation error"],"whenToPreferThis":"Choose this endpoint when you need both a trailing moving-average smoothing and missing-value imputation for geospatial tile processing time series data in a single low-cost call. It is purpose-built for tile_count and processing_seconds metrics grouped by station, making it more targeted than a general-purpose statistics API. Ideal for preprocessing pipeline data before visualization or further analysis.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:36:18.070Z","isFirstParty":false,"canonicalSlug":"geospatial-tile-processing-trailing-mean-series-explicit-missing-4c37eb59"}