# Tourism Observations — Trailing Mean Series + Adjacent Row Changes

> Tourism Observations — 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 2026-10-26).

Computes trailing moving-average series and adjacent-row delta changes for tourism observation records (visitor_count or overnight_stays) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-economics-14-146?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-26
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/tourism-observations-trailing-mean-series-adjacent-row-changes-c21469bd
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_QCqMDs0L2OFLGb9gy8jzX

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability tourism-observations-trailing-mean-series-adjacent-row-changes-c21469bd -d '<json body>'
```

Example prompt: 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?

## When to prefer this

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.

## Known failure modes

- 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

## How this service works

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.

## Output

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.

## Request schema (JSON Schema)

```json
{
 "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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/tourism-observations-trailing-mean-series-adjacent-row-changes-c21469bd/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)