# Time Series Completeness — String Normalisation + Secondary-Measure Weighted Mean

> Time Series Completeness — String Normalisation + Secondary-Measure Weighted Mean 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 up to 200 time series completeness records by performing string normalisation and secondary-measure weighted mean calculations across observed vs expected data points, producing two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-034?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-string-normalisation-secondary-measure-3371b859
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_1SrkQTkkiSWCQCzYGO9Rr

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-secondary-measure-3371b859 -d '<json body>'
```

Example prompt: I have a batch of time series completeness records — up to 200 rows with observed_points and expected_points per date and dataset group — can you run the string normalisation and secondary-measure weighted mean analysis on the observed_points field and give me both reports?

## When to prefer this

Choose this endpoint when you need two complementary analytical perspectives — string normalisation and weighted mean — on time series completeness records (observed vs expected points) in a single low-cost call. It is well suited for data quality workflows where you want to process up to 200 dated, grouped completeness rows without triggering external lookups or live data retrieval. Prefer it over general-purpose analytics when your data is already structured with row_id, dataset_group, date, observed_points, and expected_points fields.

## Known failure modes

- Fewer than 1 row or more than 200 rows submitted — rejected due to minItems/maxItems constraint
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — validation error
- Date field not matching YYYY-MM-DD pattern — schema validation failure
- Numeric fields outside allowed range (-1e12 to 1e12) — validation error
- Payment not completed — endpoint returns 402 Payment Required
- Null values in both observed_points and expected_points may produce degenerate weighted mean output

## How this service works

Time series completeness — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean 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: (1) a string normalisation report applying normalisation logic to the dataset group labels and row identifiers in the supplied records, and (2) a secondary-measure weighted mean report computing a weighted average over the selected numeric field (observed_points or expected_points) across the submitted time series completeness rows. Both reports are generated from the supplied data using local analytical engines 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-string-normalisation-secondary-measure-3371b859/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)