# Time Series Completeness Analyzer: String Normalisation + IQR Outlier Flags

> Time Series Completeness Analyzer: String Normalisation + 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).

Performs string normalisation and IQR-based outlier detection on up to 200 rows of time series completeness records (observed_points vs expected_points), returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-032?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-analyzer-string-normalisation-iqr-outlier-093a1d58
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_bKE08aWQzI6ppTQp6Lxsh

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-analyzer-string-normalisation-iqr-outlier-093a1d58 -d '<json body>'
```

Example prompt: Can you check my time series completeness data for outliers? I have 10 rows from group-a and group-b spanning October 2026 with observed_points ranging from 1 to 50 and expected_points of 10 each — run the IQR outlier flags and string normalisation report on the observed_points field.

## When to prefer this

Choose this endpoint when you need a low-cost ($0.01 USDC), self-contained, local analytical engine to simultaneously normalise string fields and detect IQR-based outliers in time series completeness data — no external data sources, live lookups, or ML predictions involved. Ideal for batch data quality checks on up to 200 rows of observed vs expected point counts across grouped time series.

## Known failure modes

- Input exceeds 200 rows — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- All values identical — IQR is zero, outlier detection may be uninformative
- Null values in numeric fields may affect IQR computation

## How this service works

Time series completeness — String normalisation + IQR outlier flags: String normalisation + 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: one covering string normalisation of the input records and one covering IQR-based outlier flags on the selected numeric field (observed_points or expected_points). Each report summarises anomalies and normalised values across up to 200 input 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-analyzer-string-normalisation-iqr-outlier-093a1d58/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)