# Time Series Completeness: Adjacent Row Changes + IQR Outlier Flags

> Time Series Completeness: 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 2026-10-19).

Analyzes time series completeness records (observed vs expected data points) for adjacent row-to-row changes and IQR-based outlier detection, returning two independent analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-157?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-adjacent-row-changes-iqr-outlier-flags-bb098a95
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_gkw-gbl20I1V4ZjvWG9c3

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 time-series-completeness-adjacent-row-changes-iqr-outlier-flags-bb098a95 -d '<json body>'
```

Example prompt: Can you run adjacent row change and IQR outlier detection on my completeness records? I have 30 daily rows with observed_points and expected_points for two dataset groups — flag anything suspicious in the observed_points field.

## When to prefer this

Choose this endpoint when you have up to 200 rows of time series completeness data (observed vs expected point counts) and need both adjacent row change detection and IQR-based outlier flagging in a single low-cost call. It is ideal for data quality monitoring pipelines, SLA audits, or anomaly triage where no external data or predictions are needed — just statistical analysis on your submitted records. Prefer this over general analytics tools when you need two complementary reports (trend changes + statistical outliers) on completeness metrics specifically.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date values not matching YYYY-MM-DD pattern — schema validation failure
- Null values in numeric fields may affect IQR computation accuracy
- Payment of 0.01 USDC not completed — 402 payment required response
- Invalid field selector (not 'observed_points' or 'expected_points') — enum validation error

## How this service works

Time series completeness — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied time series completeness records (observed_points, expected_points). 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 independent analytical reports for the supplied rows: (1) adjacent row change analysis showing period-over-period deltas in the selected field (observed_points or expected_points), and (2) IQR-based outlier flags identifying rows whose values fall outside the interquartile range bounds. No external lookups or live data — analysis is performed purely on the submitted rows.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-adjacent-row-changes-iqr-outlier-flags-bb098a95/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)