# Time Series Completeness Analyzer — Numeric Distribution & Adjacent Row Changes

> Time Series Completeness Analyzer — Numeric Distribution & 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).

Computes numeric distribution statistics and adjacent row-to-row change analysis for time series completeness records (observed vs. expected data points) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-077?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-numeric-distribution-adjacent-row-ce372040
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_caBgQFJ8sJahB2_yhg2r5

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-numeric-distribution-adjacent-row-ce372040 -d '<json body>'
```

Example prompt: I have daily time series completeness records for groups 'sensor-a' and 'sensor-b' from 2025-01-01 to 2025-01-10 — each row has an observed_points and expected_points value. Can you run the numeric distribution and adjacent row changes analysis on the observed_points field for all 10 rows?

## When to prefer this

Choose this endpoint when you have structured time series completeness data (observed vs. expected point counts) and need two quick local analytical reports — numeric distribution and adjacent row changes — without any live lookups, predictions, or external integrations. It is ideal for lightweight data quality checks on up to 200 rows at minimal cost ($0.01 USDC for both reports).

## Known failure modes

- Missing required fields (row_id, date, observed_points, expected_points) causes validation error
- Submitting more than 200 rows exceeds the maxItems limit and will be rejected
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Null values in both observed_points and expected_points may produce incomplete distribution stats
- Selecting a field value outside the enum ('observed_points' or 'expected_points') causes a schema error

## How this service works

Time series completeness — Numeric distribution + Adjacent row changes: Numeric distribution + 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

Returns two independent analytical reports: (1) a numeric distribution summary (e.g. min, max, mean, percentiles, spread) of the chosen field (observed_points or expected_points) across all supplied rows, and (2) an adjacent row change report showing value deltas between consecutive rows — both computed locally with no external lookups. Covers 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-analyzer-numeric-distribution-adjacent-row-ce372040/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)