Research & academia
TimesFM-3: A zero-shot foundation model for multivariate forecasting
TimesFM-3 combines cross-series information and known future covariates to forecast multiple time series in one forward pass.
Why it matters: When forecasts depend on related series or known future events, these inputs can be modeled together rather than ignored by a univariate forecast. A single-pass horizon and nine quantiles per target also provide forecasts with uncertainty estimates without an iterative decoding loop.