src/description.md

Data Format

• A table (CSV, XLSX, or Parquet), one column per time series.
• For uploads, use First row contains column names when the first row is a header. Otherwise columns are auto-named Series 0, 1, ….
• Optionally select a Time column before parsing. It is removed from the forecastable series and shown as real dates on the time axis. Leave it blank to index by step.
• Series longer than the model's context window keep their most recent steps.
• Pick one Target series to forecast, then optionally add multiple aligned Covariates.
• Demo examples include future-known covariates and preselect them for comparison against a univariate TiRex baseline.

Modes

• Forecast starts at time index controls the boundary between context and prediction. The model observes all selected target values before that index. Choose an earlier index to backtest against known future target values, or choose the final index to forecast after the available data.

Covariates (in Series)

• Any series you don't pick as the target can be added under Data covariates — known drivers (promotions, price, weather, related series…) that inform the forecast but are not forecast themselves.
• Known into the future (default): covariate values are provided from the context window through the full forecast horizon. This is required for scheduled/known-ahead drivers.
• History only (past): covariates condition on history only; targets are forecast beyond the chosen start using target history and covariate history only.
• When covariates are used, the forecast tab also runs a univariate TiRex baseline and overlays its median forecast for comparison.

About TiRex-2

TiRex-2 is a zero-shot multivariate forecasting model from NXAI. It forecasts multiple target variates out of the box and natively conditions on past and known covariates (calendar features, holidays, promotions, scheduled interventions). It outputs quantiles forecasts, so every prediction comes with a calibrated uncertainty band.

Citation

@misc{podest2026tirex2generalizingtirexmultivariate,
    title = {{TiRex-2}: Generalizing TiRex to Multivariate Data and Streaming},
    author = {Patrick Podest and Marco Pichler and Elias B\\\"urger and Levente Z\\\"olyomi and Bernhard Voggenberger and Wilhelm Berghammer and Daniel Klotz and Sebastian B\\\"ock and G\\\"unter Klambauer and Sepp Hochreiter},
    year = {2026},
    eprint = {2607.01204},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url = {https://arxiv.org/abs/2607.01204},
}