# Time Series Completeness: String Normalisation + Two-Measure Correlation

> Time Series Completeness: String Normalisation + 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 2026-10-28).

Analyzes up to 200 rows of time series completeness data (observed vs expected points) using string normalisation and two-measure correlation, producing 2 independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-033?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-string-normalisation-two-measure-correlation-aa570b61
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_XY6wJ2XyuozUfMplGg1Ok

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-string-normalisation-two-measure-correlation-aa570b61 -d '<json body>'
```

Example prompt: Can you analyze the completeness of my time series data — I have up to 200 rows each with a date, dataset group, observed points, and expected points — and give me both a string normalisation report and a two-measure correlation report showing how well observed tracks expected?

## When to prefer this

Choose this endpoint when you need offline, supply-side statistical analysis of time series completeness data (observed vs expected point counts) without any external data lookups or predictions. It is ideal for data quality audits, pipeline monitoring, and completeness reporting where you already have the raw rows and need two complementary analytical views (string normalisation + correlation) in a single low-cost call. Prefer this over general-purpose analytics tools when you need a structured, repeatable completeness report on up to 200 date-grouped rows.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- Malformed date strings not matching YYYY-MM-DD pattern rejected
- Numeric values outside allowed range (-1e12 to 1e12) rejected
- Empty rows array (minItems:1 not met) causes error
- Payment not processed — x402 payment of 0.01 USDC required per call

## How this service works

Time series completeness — String normalisation + Two-measure correlation: String normalisation + 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 2 independent analytical reports for the supplied time series completeness rows: (1) a string normalisation report and (2) a two-measure correlation report comparing observed_points to expected_points across the provided dates and dataset groups. No external lookups or predictions are made — all analysis is performed on the supplied data only.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-string-normalisation-two-measure-correlation-aa570b61/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)