Quickstart

The easiest way to get started is the "Getting Started" notebook, which you can also run directly in Google Colab:

If you have cloned the repository, start it locally via Pixi:

pixi run notebook

Depending on your CUDA version and use case you may need a different environment, e.g. -e example-cu128, as defined in pyproject.toml under tool.pixi.environments.

Minimal usage: predicting a simple sine wave

import torch
from tirex2 import TimeseriesType, load_model
from tirex2.plotting import plot_multivariate  # requires matplotlib to be installed

# load model
model = load_model("NX-AI/TiRex-2", device="cpu")  # use `device="cuda"` if cuda is available

# generate data - target expects time series of shape (n_targets, context_length)
context = torch.sin(torch.arange(128).float() / 8)
ts = TimeseriesType(target=context.unsqueeze(0), past_covariates=None, future_covariates=None)

# perform forecast - each forecast is of shape (n_targets, 9 quantiles, prediction_length)
forecast = model.forecast([ts], prediction_length=32, output_type="numpy")[0]

# visualize result
fig = plot_multivariate(ts, forecast, engine="matplotlib")
fig.show()

Covariate example

This example originates from the "Getting Started" notebook, showing the value of additional covariates.

from tirex2 import load_model
from tirex2.demo import Demo, plot_demo_forecast

# load model
model = load_model("NX-AI/TiRex-2", device="cpu")  # use `device="cuda"` if cuda is available

# load data
demo = Demo.create_nonstationary_demo()
ts_univariate = demo.to_timeseries_type(include_covariates=False)
ts_multivariate = demo.to_timeseries_type(include_covariates=True)

# perform forecast - each forecast is of shape (n_targets, 9 quantiles, prediction_length)
forecasts = model.forecast(
    timeseries=[ts_univariate, ts_multivariate],
    prediction_length=demo.horizon,
    output_type="numpy",
)

# visualize result
fig = plot_demo_forecast(demo, *forecasts, engine="matplotlib")
fig.show()

Next steps

• Forecasting — univariate and multivariate forecasting in depth.
• Covariates — past and future-known covariates.
• Streaming — what's open-source and what's Pro-only.
• API reference — full signatures and parameters.