# Time Series Completeness — Duplicate Key Report + IQR Outlier Flags

> Time Series Completeness — Duplicate Key Report + 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 2026-10-15).

Analyzes up to 200 time series completeness records to produce a duplicate key report and IQR-based outlier flags for observed_points or expected_points values.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-065?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-duplicate-key-report-iqr-outlier-flags-fa03bcff
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_aYd_-V453MgCwCt4dnxLn

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-duplicate-key-report-iqr-outlier-flags-fa03bcff -d '<json body>'
```

Example prompt: Check my time series completeness records for duplicate keys and IQR outlier flags on observed_points — here are my rows: row-1 group-a 2026-10-01 observed=10 expected=1, row-2 group-b 2026-10-02 observed=20 expected=2, row-3 group-a 2026-10-03 observed=15 expected=3, row-4 group-b 2026-10-04 observed=200 expected=4.

## When to prefer this

Choose this endpoint when you need both a duplicate key report AND IQR outlier detection on time series completeness data (observed_points / expected_points) in a single $0.01 call covering up to 200 rows. It is ideal for data quality checks prior to ingestion, pipeline audits, or completeness monitoring workflows where no external data lookup or prediction is needed — just fast, local statistical analysis of supplied records.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) — validation error
- Empty rows array (minItems: 1 not met) — request rejected
- Payment of 0.01 USDC not provided — 402 payment required

## How this service works

Time series completeness — Duplicate key report + IQR outlier flags: Duplicate key report + 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: (1) a duplicate key report identifying rows sharing the same key fields (row_id, dataset_group, date), and (2) IQR outlier flags for the selected numeric field (observed_points or expected_points), identifying rows that fall outside the interquartile range bounds. Both reports cover up to 200 input rows and are computed locally without 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-duplicate-key-report-iqr-outlier-flags-fa03bcff/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)