# Forecast Verification Inputs — Adjacent Row Changes + IQR Outlier Flags

> Forecast Verification Inputs — Adjacent Row Changes + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).

Analyzes supplied forecast verification data (forecast_value and observed_value) to produce adjacent row change calculations and IQR-based outlier flags across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-24-157?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/forecast-verification-inputs-adjacent-row-changes-iqr-outlier-flags-1c7423e8
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_5By9xLIMzMs3fKCjdmopn

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 forecast-verification-inputs-adjacent-row-changes-iqr-outlier-flags-1c7423e8 -d '<json body>'
```

Example prompt: I have forecast verification records for two station groups from October 2026 — rows with date, forecast_value, and observed_value — can you run the adjacent row change analysis and IQR outlier flags on the forecast_value field for up to 4 rows? Row IDs are row-1 through row-4.

## When to prefer this

Choose this endpoint when you need lightweight, local statistical analysis of forecast verification datasets — specifically adjacent row delta calculations and IQR outlier detection — without requiring live weather data access, external predictions, or professional meteorological assessment. Best suited for data quality auditing and anomaly flagging on batches up to 200 rows at very low cost ($0.01 USDC for both reports).

## Known failure modes

- More than 200 rows submitted — request exceeds the maximum item limit
- Missing required fields (row_id, date, forecast_value, observed_value) cause validation errors
- Invalid date format (non-YYYY-MM-DD) triggers schema rejection
- Numeric values outside the allowed range (±1e12) cause input rejection
- Empty rows array returns an error due to minItems:1 constraint

## How this service works

Forecast verification inputs — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied forecast verification inputs records (forecast_value, observed_value). 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 submitted rows: (1) adjacent row change calculations showing the delta between consecutive rows for the selected field, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range bounds — all computed locally without any external lookup or live data.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "station_group",
     "date",
     "forecast_value",
     "observed_value"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "station_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "forecast_value": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "observed_value": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "station_group": "group-a",
     "forecast_value": 10,
     "observed_value": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "station_group": "group-b",
     "forecast_value": 20,
     "observed_value": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "station_group": "group-a",
     "forecast_value": 15,
     "observed_value": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "station_group": "group-b",
     "forecast_value": 30,
     "observed_value": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "forecast_value",
    "observed_value"
   ],
   "type": "string",
   "default": "forecast_value"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/forecast-verification-inputs-adjacent-row-changes-iqr-outlier-flags-1c7423e8/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)