# Time Series Completeness — Base-100 Series + IQR Outlier Flags

> Time Series Completeness — Base-100 Series + 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).

Computes a base-100 normalized series and IQR-based outlier flags for time series completeness data (observed vs. expected points), returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-164?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-base-100-series-iqr-outlier-flags-22fe370f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_aPu8Rj2pNVnNKxY0-iLS_

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-base-100-series-iqr-outlier-flags-22fe370f -d '<json body>'
```

Example prompt: Analyze my time series completeness data — I have 30 rows with row IDs, dataset groups, dates, observed_points and expected_points — can you compute a base-100 normalized series starting from index 0 on the observed_points field and flag any IQR outliers across two reports?

## When to prefer this

Choose this endpoint when you have pre-collected time series completeness records (observed vs. expected data points) and need both a base-100 normalized trend view and IQR-based outlier detection in a single call. It is ideal for data quality monitoring, pipeline auditing, or coverage analysis where you already have the data locally and need fast, offline statistical computation without external lookups or predictions. Best for datasets up to 200 rows where two simultaneous analytical reports (normalization + outlier flagging) are needed at low cost.

## Known failure modes

- Fewer than 1 or more than 200 rows provided — returns validation error
- Invalid date format (not YYYY-MM-DD) — returns schema validation error
- base_index out of range (0–199) or exceeds actual row count — returns error
- Null values in observed_points/expected_points may affect IQR and base-100 calculations
- Field value not in enum ['observed_points','expected_points'] — returns validation error

## How this service works

Time series completeness — Base-100 series + IQR outlier flags: Base-100 series + 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 analytical reports: (1) a base-100 normalized series for the selected field (observed_points or expected_points), rebased to 100 at the specified index, showing relative changes across the time series; and (2) IQR-based outlier flags identifying rows where values fall beyond 1.5× the interquartile range, indicating anomalous completeness observations.

## 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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-base-100-series-iqr-outlier-flags-22fe370f/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)