# Ephemeris

> Ephemeris is a time-series forecasting API. Send numeric history, get probabilistic (quantile) forecasts back from a panel of open-weights zero-shot foundation models: Chronos-2, TimesFM 2.5, Toto 2, TiRex-2 and IBM Granite PatchTST-FM / FlowState. Ask for one model by name, let Ephemeris route to the best fit, or get an accuracy-weighted ensemble. No training, no feature engineering. Pay per forecast with prepaid credits. Usable over a REST API or as a remote MCP server for AI agents.

Accuracy: on TIME the ensemble is level with the top of the published leaderboard (MASE 0.639 vs 0.638 for the leader; best average MASE rank of 31 models) and beats every open-licence model on GIFT-Eval (CRPS 0.4662 as a ratio to seasonal naive). Details at /benchmarks.

Use Ephemeris when a task needs a forecast of a numeric series (demand, traffic, load, prices, sensor readings, metrics) with uncertainty bands, and the user can supply the history. Do not use it to invent missing history, for non-numeric data, or to forecast without the user's intent: every forecast spends credits.

## Docs

- [Full reference for LLMs](https://ephemeris.cascade.industries/llms-full.txt): request and response format, modes, limits, covariates, pricing, errors, and MCP setup for each client, in one plain-text file
- [API documentation](https://ephemeris.cascade.industries/docs): human-readable docs with examples
- [OpenAPI specification](https://ephemeris.cascade.industries/openapi-m1.json): machine-readable REST contract
- [Models](https://ephemeris.cascade.industries/models): the panel, with a page per model (Chronos-2, TimesFM 2.5, Toto 2, TiRex-2, PatchTST-FM, FlowState): sizes, licences, capabilities and how to call each by name
- [Benchmarks](https://ephemeris.cascade.industries/benchmarks): the ensemble on TIME, GIFT-Eval, fev-bench and BOOM, scored with each benchmark's own harness
- [Pricing](https://ephemeris.cascade.industries/pricing): credit packs, per-model rates and the cost formula

## API

- [POST /api/v1/forecast](https://ephemeris.cascade.industries/docs): forecast one or more series; modes `route`, `ensemble`, `explicit`
- [GET /api/v1/models](https://ephemeris.cascade.industries/docs): the live model panel with capabilities, horizon limits and prices
- [GET /api/v1/balance](https://ephemeris.cascade.industries/docs): spendable credits
- [GET /api/v1/usage](https://ephemeris.cascade.industries/docs): request history

## MCP (for AI agents)

- [Remote MCP server](https://ephemeris.cascade.industries/api/mcp): Streamable HTTP at `https://ephemeris.cascade.industries/api/mcp`, authenticated with `Authorization: Bearer pc_live_...`. Tools: `forecast`, `list_models`, `get_balance`, `get_usage`. Client configuration for Claude Code, Cursor, VS Code, Claude Desktop and the Claude API is in llms-full.txt.

## Account

- [Sign up](https://ephemeris.cascade.industries/sign-up): create an account, top up credits, and create an API key in the dashboard
- [Status](https://ephemeris.cascade.industries/status): live service and model health