theforecastingcompany/t0-alpha

t0

Open-weights time-series forecasting foundation model from The Forecasting Company. t0 is a transformer-based model that produces probabilistic multi-horizon forecasts and natively operates on multiple covariates. t0-alpha is our first iteration of the model.

You can use t0 on Retrocast, our platform for forecasting on your own data. You can also compare forecast across different open-weight models.

t0 forecasting French national electricity demand in Retrocast. Data: Enedis open data.

📈 Forecasting with covariates

t0 leverages covariate information, in the past and future when available, to improve its forecast.

Without covariates	With covariates

Data: Medic'AM, monthly drug reimbursements from the French national health insurance.

The Quickstart below shows the API for both a plain univariate forecast and a multivariate forecast that conditions on historical and known-future covariates.

🚀 Quickstart

pip install tfc-t0

The simplest path is a univariate forecast through predict:

import torch
from t0 import T0Forecaster

model = T0Forecaster.from_pretrained("theforecastingcompany/t0-alpha").eval()

context = torch.randn(4, 512)  # 4 series, 512 past timesteps
out = model.predict(context, horizon=64, quantiles=[0.1, 0.5, 0.9])
out.quantiles  # (4, 64, 3)
out.median     # (4, 64)

predict accepts numpy arrays. 1-D contexts are auto-promoted to a single-row batch. NaN values in the context are treated as missing observations.

Forecasting with covariates

Anything you know over the past goes in context — alongside the target, extra variates attend to it and are forecast together. Anything you know over the future (calendar features, planned promotions, weather forecasts) goes in future_covariates, shaped [B, F, context + horizon]; the model conditions on it but does not forecast it.

import torch
from t0 import T0Forecaster

model = T0Forecaster.from_pretrained("theforecastingcompany/t0-alpha").eval()

context = torch.randn(2, 512)                    # 2 series, 512 past timesteps
future_covariates = torch.randn(2, 3, 512 + 64)  # 3 covariates known over context + horizon

out = model.predict(
    context,
    horizon=64,
    quantiles=[0.1, 0.5, 0.9],
    future_covariates=future_covariates,
)
out.quantiles  # (2, 64, 3)
out.median     # (2, 64)

🏗️ Architecture

t0 is a decoder-style patch transformer that alternates time and covariate attention layers. It predicts 5 quantiles (0.1, 0.25, 0.5, 0.75, 0.9), decoding multiple horizons in parallel — up to 1024 timesteps in one forward pass — and falling back on autoregressive rollout for longer horizons.

Parameters	~102M
Layers	24
Embedding dim	512
Feedforward dim	2048
Attention heads	8
Patch size	32
Quantile levels	0.1, 0.25, 0.5, 0.75, 0.9

🧬 Lineage

t0 builds on ideas — and in places, code — from open-source forecasting models. We gratefully acknowledge:

• Toto by Datadog (repo) & Chronos-2 by Amazon (repo) — factorizing attention in the time and variates dimension.
• TiRex by NXAI (repo) — contiguous patch masking.

Code-level attributions are listed in NOTICE, all under Apache-2.0.

🧰 Public API

• T0Forecaster — nn.Module with from_pretrained / save_pretrained (via huggingface_hub.PyTorchModelHubMixin) and the user-facing predict(context, horizon, quantiles, future_covariates).
• T0Config — frozen dataclass; T0Config.medium() is the published configuration.

📚 Citation

@misc{tfc-t0,
  title  = {t0: A time-series forecasting foundation model},
  author = {The Forecasting Company},
  year   = {2026},
  url    = {https://huggingface.co/theforecastingcompany/t0-alpha},
}

⚖️ License

Apache-2.0 — see LICENSE and NOTICE.