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Documentation

Overview

Welcome to The Forecasting Company API. We expose our own, as well as open-source foundation models, behind a unified REST API at https://api.retrocast.com.

This page covers the quick start, authentication, the available models and the supported frequencies. The rest of the documentation:

• Endpoints — which endpoint to reach for, and how to read a forecast, poll a job and run a backtest. Parameters, schemas and worked examples live in the API Reference.
• Covariates — exogenous variables you provide, and holidays, events and temporal features we provide.
• Model-specific configuration — the model_config overrides accepted by Auto-ARIMA, Auto-ETS and Chronos-2.
• Python SDK — the higher-level theforecastingcompany client.
• Error handling — status codes and how to reach us.

Quick start

Code
curl -X POST "https://api.retrocast.com/forecast?model=tabpfn-ts" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "series": [{
      "index": ["2024-01-01","2024-01-02","2024-01-03","2024-01-04","2024-01-05"],
      "target": [125, 120, 140, 135, 133]
    }],
    "horizon": 5,
    "freq": "D",
    "quantiles": [0.1, 0.5, 0.9]
  }'

Or with the Python SDK:

Code
pip install theforecastingcompany

Code
from theforecastingcompany import TFCClient, TFCModels

client = TFCClient()  # reads TFC_API_KEY from environment

forecast_df = client.forecast(
    train_df,
    model=TFCModels.TabPFN_TS,
    horizon=12,
    freq="W",
    quantiles=[0.1, 0.5, 0.9],
)

Authentication

All API requests require a Bearer token. Obtain your API key at https://docs.retrocast.com/settings/api-keys.

Include it in every request header:

Code
Authorization: Bearer YOUR_API_KEY

Available models

Model	Description	Covariates?	Forecast?	Backtest?
t0-alpha	The Forecasting Company's model	✅	✅	✅
tfc-global	Global model for time series	✅	✅	✅
tabpfn-ts	Foundation model from Uni. of Freiburg	✅	✅	✅
moirai-2	Salesforce's Moirai v.2	✅	✅	✅
timesfm-2	Google's TimesFM 2.0	Future covariates only	✅	✅
timesfm-2p5	Google's TimesFM 2.5	Future covariates only	✅	✅
chronos-2	Amazon's Chronos-2	✅	✅	✅
tirex	NXAI's model based on xLSTMs	❌	✅	✅
aarima	Auto-ARIMA	Soon	✅	✅
aets	Auto-ETS	Soon	✅	✅
toto-2	Datadog's Toto v.2	❌	✅	✅

Models that support exogenous variables (covariates)

Only tabpfn-ts, moirai-2, and chronos-2 support full exogenous variables. timesfm-2 and timesfm-2p5 support future covariates only. tfc-global supports all covariate types including string covariates and static variables.

Models that support string covariates

Only tabpfn-ts and tfc-global support non-numeric (string) covariates. All other models require numeric covariates only.

Supported frequencies

The freq field must be one of:

Value	Meaning
10S	10 seconds
min	1 minute
5min	5 minutes
10min	10 minutes
15min	15 minutes
30min	30 minutes
H	Hourly
D	Daily
W	Weekly
M	Monthly
Q	Quarterly
Y	Yearly