TiRex-2 enables efficient streaming multivariate forecasting with constant per-patch inference cost and zero-shot generalization, solving the quadratic complexity problem of Transformer-based time series models.
TiRex-2 is a time series foundation model built on xLSTM that handles multiple variables and streaming data efficiently. Unlike Transformer-based models that slow down with longer sequences, TiRex-2 uses a memory-based recurrent design that processes new data at constant cost, even when variables evolve together and some future values are known in advance.