# Time Series Completeness — Record Quality Audit + Daily Sum Series

> Time Series Completeness — Record Quality Audit + Daily Sum 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 two local analytical reports (record quality audit and daily sum series) on up to 200 time series completeness records containing observed and expected data points.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-009?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-record-quality-audit-daily-sum-series-5e8716fe
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_fwaDbT_OBImxWOitvcwEb

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-record-quality-audit-daily-sum-series-5e8716fe -d '<json body>'
```

Example prompt: Can you run a record quality audit and daily sum series on my time series completeness data? I have rows for dataset groups 'sensors-a' and 'sensors-b' from 2024-01-01 to 2024-01-07, with observed_points and expected_points for each day — analyze the observed_points field.

## When to prefer this

Choose this endpoint when you need both a record-level quality audit AND a daily aggregated sum series in a single call for time series completeness data (observed vs expected points). It is ideal for batch analysis of up to 200 rows without requiring any external data sources, live lookups, or predictions — purely local computation on your supplied data. Prefer this over single-report endpoints when you want two complementary views (row-level quality + daily totals) for the cost of one call.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) on any row — validation error
- Invalid date format (must match YYYY-MM-DD) — schema validation failure
- Null values in numeric fields may affect sum series calculations
- Payment of 0.01 USDC not completed — x402 payment required error

## How this service works

Time series completeness — Record quality audit + Daily sum series: Record quality audit + Daily sum series 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 record quality audit highlighting issues, anomalies, or quality flags across the supplied completeness rows, and (2) a daily sum series aggregating the specified field (observed_points or expected_points) by date. Both reports are computed locally with no external lookups or predictions, covering 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-record-quality-audit-daily-sum-series-5e8716fe/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)