# Time Series Completeness — Daily Sum Series + IQR Outlier Flags

> Time Series Completeness — Daily Sum Series + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Computes daily aggregated sums and flags statistical outliers using IQR analysis for up to 200 time series completeness records containing observed and expected data points.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-140?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-daily-sum-series-iqr-outlier-flags-f9193af4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_7KDPb7Yo1wxcqKGjQykSm

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-daily-sum-series-iqr-outlier-flags-f9193af4 -d '<json body>'
```

Example prompt: Analyze my time series completeness data — here are 200 rows with row_id, dataset_group, date, observed_points, and expected_points fields — compute the daily sum series and flag any IQR outliers on the observed_points field.

## When to prefer this

Choose this endpoint when you have pre-collected time series completeness records (observed vs expected data points) and need both a daily aggregated sum view and statistical outlier detection in a single low-cost call. It is ideal for data quality pipelines, ETL monitoring, and sensor coverage audits where you supply the data directly and need offline statistical analysis rather than live lookups or predictions. At $0.01 USDC for two reports covering up to 200 rows, it is cost-effective for batch completeness QA tasks.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) on any row — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Insufficient data points for meaningful IQR calculation (very few rows) — degenerate statistical output
- null values in observed_points or expected_points may affect aggregation accuracy

## How this service works

Time series completeness — Daily sum series + IQR outlier flags: Daily sum series + IQR outlier flags 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 analytical reports sharing local computation engines: (1) a daily sum series aggregating observed_points or expected_points by date across dataset groups, and (2) IQR-based outlier flags identifying which rows or dates fall outside the interquartile range. No external lookups or predictions are made; all analysis is performed on the supplied data up to 200 rows.

## 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-daily-sum-series-iqr-outlier-flags-f9193af4/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)