# Time Series Completeness — Adjacent Row Changes + Two-Measure Correlation

> Time Series Completeness — Adjacent Row Changes + Two-Measure Correlation 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 by computing adjacent row changes and two-measure correlation between observed_points and expected_points for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-158?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-adjacent-row-changes-two-measure-correlation-4860c200
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ennXyOrr2niBoap2mnai9

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-adjacent-row-changes-two-measure-correlation-4860c200 -d '<json body>'
```

Example prompt: Can you run an adjacent-row-change and correlation analysis on my time series completeness data? I have 30 rows with dates from 2025-01-01 to 2025-01-30, dataset groups 'sensor-a' and 'sensor-b', and daily observed_points and expected_points — use observed_points as the field.

## When to prefer this

Choose this endpoint when you have pre-collected time series completeness records (observed vs expected point counts) and need both a sequential change analysis and a correlation metric in a single low-cost call. It is ideal for data quality pipelines, SLA monitoring, and completeness audits where you already hold the raw rows and want offline statistical summaries without any external data fetching or prediction.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date values not matching YYYY-MM-DD pattern — schema validation failure
- Null values in both measure columns preventing correlation computation
- Single-row input preventing adjacent change computation

## How this service works

Time series completeness — Adjacent row changes + Two-measure correlation: Adjacent row changes + Two-measure correlation 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 sharing local engines: (1) an adjacent row changes report showing how observed_points or expected_points shift between consecutive rows, and (2) a two-measure correlation report quantifying the statistical relationship between observed_points and expected_points across all supplied rows. Covers up to 200 input rows with no external lookups or live data.

## 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-adjacent-row-changes-two-measure-correlation-4860c200/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)