# Time Series Completeness — Grouped Numeric Summary + Adjacent Row Changes

> Time Series Completeness — Grouped Numeric Summary + Adjacent Row Changes 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).

Analyzes time series completeness records (observed vs expected data points) to produce grouped numeric summaries and adjacent row change reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-091?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-grouped-numeric-summary-adjacent-row-changes-549c2d97
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_6oF0BDEYDtAfjRTt8gEUJ

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-grouped-numeric-summary-adjacent-row-changes-549c2d97 -d '<json body>'
```

Example prompt: Can you run a grouped numeric summary and adjacent row change report on my time series completeness data? I have rows with dataset_group, date, observed_points, and expected_points — here are up to 200 rows — and I want to analyze the observed_points field.

## When to prefer this

Choose this endpoint when you have pre-collected time series completeness records and need two complementary statistical views — grouped summaries and sequential row differences — in a single inexpensive call. It is ideal for data quality monitoring, pipeline auditing, or completeness dashboards where you supply the data and want fast, offline analytical computation without any external data fetch or prediction.

## Known failure modes

- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) will cause validation errors
- Exceeding 200 rows returns an error or truncation
- Malformed date strings not matching YYYY-MM-DD pattern will fail schema validation
- Null values in the selected field may affect numeric summary calculations
- Empty rows array is rejected (minItems: 1)

## How this service works

Time series completeness — Grouped numeric summary + Adjacent row changes: Grouped numeric summary + Adjacent row changes 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

Two analytical reports based on the supplied rows: (1) a grouped numeric summary showing aggregate statistics (e.g. count, min, max, mean) of the selected field broken down by dataset_group; and (2) an adjacent row changes report showing the difference in the selected field between consecutive rows. Both reports are computed locally with no external lookups.

## 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-grouped-numeric-summary-adjacent-row-changes-549c2d97/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)