# Time Series Completeness — String Normalisation + Explicit Missing-Value Fill

> Time Series Completeness — String Normalisation + Explicit Missing-Value Fill 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).

Normalises string fields and fills explicit missing values (zero, mean, or median) in time series completeness records containing observed and expected data points, processing up to 200 rows with 2 independent reports per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-037?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-explicit-missing-value-40f52811
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ncGcw3wF_9rV9wbBFV5vg

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-explicit-missing-value-40f52811 -d '<json body>'
```

Example prompt: Can you clean up my time series completeness data — I have up to 50 rows with observed_points and expected_points across groups like 'group-a' and 'group-b' (dates in YYYY-MM-DD format), and I need you to normalise the string fields and fill any missing observed_points using median imputation, then give me 2 analytical reports?

## When to prefer this

Choose this endpoint when you need offline, local analytical processing of time series completeness records (observed vs expected data points) that require both string field normalisation and explicit missing value imputation in a single call. It is ideal for small-to-medium batch jobs (up to 200 rows) where you want two independent reports and no external lookups or predictions. Prefer it over generic data-cleaning services when your data specifically tracks observed_points and expected_points across date-keyed, group-labelled rows.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Invalid date format (not YYYY-MM-DD) — row fails validation
- Missing required fields (row_id, date, observed_points, expected_points) — row rejected
- Numeric values outside allowed range (±1,000,000,000,000) — validation error
- Empty rows array — minItems:1 constraint violated
- Invalid method enum value — must be zero, mean, or median
- Invalid field enum value — must be observed_points or expected_points

## How this service works

Time series completeness — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill 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 string-normalised and missing-value-filled versions of the submitted time series completeness records, including processed observed_points and expected_points values per row, with no live lookups or external data enrichment.

## 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-string-normalisation-explicit-missing-value-40f52811/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)