{"uid":"cap_QCqMDs0L2OFLGb9gy8jzX","slug":"tourism-observations-trailing-mean-series-adjacent-row-changes-c21469bd","name":"Tourism Observations — Trailing Mean Series + Adjacent Row Changes","description":"Tourism observations — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes for supplied tourism observations records (visitor_count, overnight_stays). 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-economics-14-146?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","region","date","visitor_count","overnight_stays"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"region":{"type":"string","default":"","maxLength":80},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"visitor_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"overnight_stays":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","region":"group-a","row_id":"row-1","visitor_count":10,"overnight_stays":1},{"date":"2026-10-02","region":"group-b","row_id":"row-2","visitor_count":20,"overnight_stays":2},{"date":"2026-10-03","region":"group-a","row_id":"row-3","visitor_count":15,"overnight_stays":3},{"date":"2026-10-04","region":"group-b","row_id":"row-4","visitor_count":30,"overnight_stays":4}],"maxItems":200,"minItems":1},"field":{"enum":["visitor_count","overnight_stays"],"type":"string","default":"visitor_count"},"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_D30Q8U03FH9Uq5nrjSaZ2","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 trailing moving-average series and adjacent-row delta changes for tourism observation records (visitor_count or overnight_stays) across up to 200 rows.","exampleAgentPrompt":"I have 50 rows of regional tourism data with visitor_count and overnight_stays — can you compute the trailing mean series and adjacent row changes for visitor_count using a window of 5, and give me both reports?","exampleUseCases":[{"title":"Regional visitor count trend smoothing","prompt":"I've got daily visitor_count observations for 10 tourism regions over the past quarter — run a trailing mean with a window of 7 and also show me the adjacent row changes so I can spot where counts jumped."},{"title":"Overnight stays delta detection","prompt":"Can you analyze these 120 rows of hotel overnight_stays records and produce both the rolling 3-period mean series and the adjacent row-to-row differences so I can identify abrupt shifts?"},{"title":"Multi-region tourism seasonality analysis","prompt":"I have tourism data for group-a and group-b regions by date — compute the trailing mean and consecutive row changes for visitor_count using a window of 4 so I can compare seasonal patterns across regions."}],"resultDescription":"Returns two analytical reports for the supplied tourism rows: (1) a trailing mean series showing the rolling average of the selected field (visitor_count or overnight_stays) over the specified window, and (2) adjacent row changes showing the delta between each consecutive row for the selected field. Both reports cover up to 200 input rows and are computed locally with no external lookups.","failureModes":["Fewer than 1 row supplied — validation error","More than 200 rows submitted — rejected with max-items error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Window size larger than number of rows — may produce sparse or empty trailing mean results","Invalid field value (not visitor_count or overnight_stays) — enum validation error","Null values in the selected numeric field — may produce null entries in output series"],"whenToPreferThis":"Choose this endpoint when you have pre-collected tourism observation records and need both a smoothed rolling-average series and consecutive-row difference analysis in a single call. It is ideal for offline/batch analytical workflows where data is already gathered and you need two complementary time-series statistics (trend smoothing + change detection) without any live data fetching, prediction, or external enrichment. Prefer it over generic analytics APIs when working specifically with visitor_count or overnight_stays fields in a regional tourism context.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T12:31:43.422Z","isFirstParty":false,"canonicalSlug":"tourism-observations-trailing-mean-series-adjacent-row-changes-c21469bd"}