# Time Series Completeness Analyzer — Record Quality Audit, Exact Deduplication & Base-100 Series

> Time Series Completeness Analyzer — Record Quality Audit, Exact Deduplication & Base-100 Series 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).

Runs three analytical reports on time series completeness data: a record quality audit, exact row deduplication, and a base-100 indexed series — all from supplied rows, up to 200 records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-218?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-record-quality-audit-exact-4aaa5022
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_CG5Gbz0zpeVjXjPXWCznI

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-record-quality-audit-exact-4aaa5022 -d '<json body>'
```

Example prompt: Can you run a quality audit, exact deduplication check, and base-100 series on my time series completeness data? I have 4 rows with dataset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, observed_points of 10, 20, 15, 30 and expected_points of 1, 2, 3, 4 — index the base-100 series on the observed_points field starting from row 0.

## When to prefer this

Choose this endpoint when you need three combined analytical operations — quality audit, deduplication, and base-100 indexing — on time series completeness data (observed vs expected points) in a single call for up to 200 rows. It is a pure local computation endpoint with no external lookups, making it fast, deterministic, and suitable for data pipelines that need repeatable results. Prefer it over separate single-purpose tools when you want all three reports in one affordable call ($0.01 USDC).

## Known failure modes

- Exceeds 200-row limit — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- base_index out of range (must be 0–199 and within supplied row count) — error
- Null values in observed_points or expected_points may affect completeness calculations
- field enum value must be exactly 'observed_points' or 'expected_points' — other values rejected

## How this service works

Time series completeness — Record quality audit + Exact row deduplication + Base-100 series: Record quality audit + Exact row deduplication + Base-100 series for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Returns three independent analytical reports: (1) a record quality audit flagging malformed, null, or suspicious rows; (2) an exact row deduplication report identifying and removing repeated records; and (3) a base-100 indexed series for the selected field starting from the specified row index — all computed locally from the supplied 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"
  },
  "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-analyzer-record-quality-audit-exact-4aaa5022/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)