# Time Series Completeness — String Normalisation + Numeric Row Ordering

> Time Series Completeness — String Normalisation + Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).

Analyzes up to 200 time series completeness records, applying string normalisation and numeric row ordering to produce two independent analytical reports on observed vs expected data points.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-025?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-string-normalisation-numeric-row-ordering-6de5fd22
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_-wBfNzgSRLsK_VoJk5JVK

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-numeric-row-ordering-6de5fd22 -d '<json body>'
```

Example prompt: I have a batch of time series completeness records — up to 50 rows with dataset groups like 'sensor-a' and 'sensor-b', dates in YYYY-MM-DD format, and both observed_points and expected_points values — can you run string normalisation and numeric row ordering on the observed_points field and give me the two analytical reports?

## When to prefer this

Choose this endpoint when you need lightweight, offline string normalisation and numeric ordering on time series completeness records (observed vs expected points) for up to 200 rows, and want two bundled analytical reports at minimal cost ($0.01 USDC). It is not suitable for live data lookups, predictions, or datasets exceeding 200 rows.

## Known failure modes

- Exceeding 200 rows causes rejection — keep batch to maxItems limit
- Missing required fields (row_id, date, observed_points, expected_points) will cause validation errors
- Dates not matching YYYY-MM-DD pattern will be rejected
- Null values in numeric fields are allowed but may affect ordering output
- Invalid field selector (not 'observed_points' or 'expected_points') will fail enum validation

## How this service works

Time series completeness — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering 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 independent analytical reports covering: (1) string-normalised representations of the dataset group and row fields, and (2) numerically ordered rows based on the selected numeric field (observed_points or expected_points), derived entirely from the supplied input data without any live lookups or external 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-string-normalisation-numeric-row-ordering-6de5fd22/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)