# PennyRail Naive Baseline Forecaster

> PennyRail Naive Baseline Forecaster is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-15).

Generates mean, naive, and drift baseline time-series forecasts with 95% prediction intervals as a sanity-check floor before using more complex models.

## Facts

- Endpoint: POST https://pennyrail.vercel.app/api/bazaar/forecast-naive
- Price: $0.001/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/pennyrail-naive-baseline-forecaster-b8e86dd5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_uXbW5pSififRyVVrtd00q

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 pennyrail-naive-baseline-forecaster-b8e86dd5 -d '<json body>'
```

Example prompt: Run a naive baseline forecast on my monthly sales figures — I have 24 months of historical data and want to see the mean, naive, and drift forecasts with 95% prediction intervals so I can decide if I need a fancier model.

## When to prefer this

Choose this endpoint when you need a cheap, fast sanity-check forecast before committing to expensive or complex time-series models. It is ideal as a benchmark baseline, a first-pass prediction, or when the cost of a sophisticated model is not yet justified. Prefer this over ML-based forecasting APIs when simplicity, speed, and cost ($0.001/call) matter most.

## Known failure modes

- Insufficient historical data points causing degenerate or missing forecasts
- Malformed body payload resulting in a 400 validation error
- Missing required fields in the input body causing rejection
- Payment not settled or x402 payment header missing causing 402 response
- Body type mismatch between declared bodyType and actual payload format

## How this service works

Generate mean, naive and drift baseline forecasts with simple 95% prediction intervals. Useful as a sanity floor before paying for a more complex forecasting model. Exact-match PennyRail gap-arbitrage route.

## Output

Returns mean, naive, and drift baseline forecast values along with 95% prediction intervals for each method, providing a statistical floor against which more sophisticated models can be benchmarked.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object",
   "required": [
    "type",
    "method",
    "bodyType",
    "body"
   ],
   "properties": {
    "body": {
     "type": "object",
     "description": "Input for Naive baseline forecasts.",
     "additionalProperties": true
    },
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "POST"
     ],
     "type": "string"
    },
    "bodyType": {
     "enum": [
      "json",
      "form-data",
      "text"
     ],
     "type": "string"
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object"
    }
   }
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/pennyrail-naive-baseline-forecaster-b8e86dd5/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from pennyrail.vercel.app](https://www.zero.xyz/host/pennyrail.vercel.app/llms.txt)