# Time Series Completeness Analyzer — Record Quality Audit + Deduplication + Histogram

> Time Series Completeness Analyzer — Record Quality Audit + Deduplication + Histogram 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 analytics reports on time series completeness data (observed vs expected points): record quality audit, exact row deduplication, and equal-width histogram — all on supplied rows, up to 200, with no external lookups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-219?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-deduplication-f453ace8
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Y57rTClF0nHMcK_zut6fW

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-deduplication-f453ace8 -d '<json body>'
```

Example prompt: Can you run a record quality audit, deduplicate exact rows, and produce a 10-bin histogram on my time series completeness data for dataset group 'sensor-network'? Here are the rows: 2024-01-01 through 2024-01-05 for row IDs r1–r5, with observed_points ranging from 80 to 100 and expected_points all set to 100 — use observed_points as the histogram field.

## When to prefer this

Choose this endpoint when you need three analytics operations in one call on time series completeness data (observed vs expected points): record quality auditing, exact deduplication, and histogram profiling. It is ideal for lightweight, self-contained data quality checks on batches up to 200 rows without needing external lookups, predictions, or live data connections. Prefer it over general-purpose analytics when you specifically need the combination of quality audit + dedup + histogram on completeness-style records at low cost ($0.01 per call).

## Known failure modes

- Rows exceed 200-item limit — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Invalid date format (not YYYY-MM-DD) — schema rejection
- bins value outside 1–30 range — validation error
- Numeric values outside ±1,000,000,000,000 — schema rejection
- Empty rows array — request rejected

## How this service works

Time series completeness — Record quality audit + Exact row deduplication + Equal-width histogram: Record quality audit + Exact row deduplication + Equal-width histogram 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 for the supplied rows: (1) a record quality audit flagging issues like nulls or out-of-range values, (2) an exact row deduplication report identifying repeated records, and (3) an equal-width histogram over the selected numeric field (observed_points or expected_points) with the specified number of bins. No external data is fetched — all analysis is performed on the submitted rows.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "bins": {
   "type": "integer",
   "default": 5,
   "maximum": 30,
   "minimum": 1
  },
  "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-record-quality-audit-deduplication-f453ace8/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)