# Transaction Fee Observations — Daily Sum Series + Explicit Missing-Value Fill

> Transaction Fee Observations — Daily Sum Series + 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 2026-10-26).

Computes a daily sum time series and fills missing values for transaction fee observation records (fee_units, transaction_count) using a chosen fill method.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-finance-03-145?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-26
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transaction-fee-observations-daily-sum-series-explicit-missing-value-34a594dd
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_XKeqad6X7PUzpPgGPzdzT

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 transaction-fee-observations-daily-sum-series-explicit-missing-value-34a594dd -d '<json body>'
```

Example prompt: I have transaction fee records for a bunch of dates — can you compute the daily sum series for fee_units and fill any missing values using median imputation? Here are up to 200 rows with row_id, instrument_group, date, fee_units, and transaction_count fields.

## When to prefer this

Choose this endpoint when you have up to 200 rows of transaction fee observation records and need both a daily sum aggregation and missing-value imputation in a single call. It is ideal for financial analysts who need to clean and summarize fee or transaction count time series data without setting up local infrastructure. Prefer this over general-purpose analytics tools when you want a lightweight, pay-per-use computation that handles two reports (daily sum + null fill) simultaneously for fee or count fields.

## Known failure modes

- Input rows array is empty or exceeds 200 items — validation error
- Date field does not match YYYY-MM-DD format — schema validation rejection
- fee_units or transaction_count values outside allowed range (-1T to 1T) — rejected
- Missing required fields (row_id, instrument_group, date) — request fails
- Invalid fill method or field enum value — schema validation error
- Payment of 0.01 USDC not completed — request not processed

## How this service works

Transaction fee observations — Daily sum series + Explicit missing-value fill: Daily sum series + Explicit missing-value fill for supplied transaction fee observations records (fee_units, transaction_count). 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 analytical reports: (1) a daily sum series aggregating the target field (fee_units or transaction_count) by date, and (2) the same dataset with explicit missing values filled using the chosen method (zero, mean, or median). Up to 200 input rows supported, processed 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",
     "instrument_group",
     "date",
     "fee_units",
     "transaction_count"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "fee_units": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "instrument_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "transaction_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "fee_units": 10,
     "instrument_group": "group-a",
     "transaction_count": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "fee_units": 20,
     "instrument_group": "group-b",
     "transaction_count": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "fee_units": 15,
     "instrument_group": "group-a",
     "transaction_count": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "fee_units": 30,
     "instrument_group": "group-b",
     "transaction_count": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "fee_units",
    "transaction_count"
   ],
   "type": "string",
   "default": "fee_units"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/transaction-fee-observations-daily-sum-series-explicit-missing-value-34a594dd/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)