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Local Event Calendars — Trailing Mean Series & Adjacent Row Changes
x402
Local Event Calendars — Trailing Mean Series & Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Computes trailing mean time series and adjacent row changes for local event calendar metrics (listed_events, updated_events) across up to 200 supplied rows, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Local event calendars — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes for supplied local event calendars records (listed_events, updated_events). 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.
Output
Two analytical reports: (1) a trailing mean series showing the rolling average of the selected field (listed_events or updated_events) over the specified window, and (2) adjacent row changes showing the delta between consecutive rows — both computed against the supplied dataset of up to 200 rows, segmented by channel_group and date.
Example use cases
Event listing trend detection
“I have 80 rows of daily local event calendar data with listed_events counts per channel group from January through March. Can you run a trailing mean with a window of 14 and show me the adjacent row-over-row changes for listed_events so I can spot any unusual spikes or drops?”
Updated events momentum analysis
“I want to understand how event update activity is changing week over week across my channel groups. I have 150 rows of calendar data — can you compute a rolling mean series with window size 5 and the adjacent changes for the updated_events field?”
Multi-group calendar performance review
“We have two channel groups, group-a and group-b, with 60 rows of daily event calendar records. Can you generate the trailing mean series and adjacent row changes for listed_events using a window of 4, so I can compare activity patterns between the groups?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-news-19-146?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pre-collected local event calendar time-series data and need offline statistical summaries — specifically trailing moving averages and adjacent row deltas — without requiring live data lookup, external enrichment, or prediction. It is best suited for batch analytical workflows on small-to-medium datasets (up to 200 rows) where cost efficiency ($0.01 for two reports) matters and the two specific report types (trailing mean + row changes) are the desired output.
Side effects
- More than 200 rows submitted — request rejected with max items exceeded error
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Window size larger than number of rows leads to insufficient data for computation
- Null or missing required fields (row_id, date) cause row rejection
- Invalid field enum value other than 'listed_events' or 'updated_events' causes parameter error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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