# Time Series Completeness — IQR Outlier Flags + Date by Category Pivot

> Time Series Completeness — IQR Outlier Flags + Date by Category Pivot 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) by running IQR-based outlier detection and a date-by-category pivot table, returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-178?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-iqr-outlier-flags-date-by-category-pivot-f170f541
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_X6WWLiIzX2q6Gu8E2wWj2

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-iqr-outlier-flags-date-by-category-pivot-f170f541 -d '<json body>'
```

Example prompt: Can you run IQR outlier detection and a date-by-category pivot on my time series completeness data? I have rows for group-a and group-b from 2026-10-01 to 2026-10-04 with observed_points and expected_points, and I want to flag outliers on the observed_points field.

## When to prefer this

Choose this endpoint when you need both IQR-based statistical outlier detection AND a date-by-category pivot table on time series completeness data in a single cost-effective call ($0.01 USDC for both reports). Ideal for data quality monitoring pipelines, ETL observability dashboards, or any scenario where you track observed vs expected data point counts across multiple groups over time. Prefer this over general analytics tools when your data is already structured as completeness records with dataset_group, date, observed_points, and expected_points fields.

## Known failure modes

- Fewer than 1 row supplied — request rejected (minItems: 1)
- More than 200 rows supplied — request rejected (maxItems: 200)
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Null values in both observed_points and expected_points may reduce IQR calculation reliability
- Invalid field enum value (not 'observed_points' or 'expected_points') — schema rejection
- Payment of 0.01 USDC not provided — x402 payment required response

## How this service works

Time series completeness — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot 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 analytical reports: (1) IQR outlier flags identifying which rows have values outside the interquartile range for the selected field (observed_points or expected_points), and (2) a date-by-category pivot table cross-tabulating the selected metric across dates and dataset_group values. Both reports are computed locally with no external lookups.

## 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-iqr-outlier-flags-date-by-category-pivot-f170f541/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)