TiRex-2是首个同时支持多变量流式预测和已知未来协变量的时间序列基础模型，零样本刷新GIFT-Eval和fev-bench榜单，每步计算量不变。做在线时序预测的朋友可以看看。
TiRex-2是基于xLSTM的循环时间序列基础模型，将单变量TiRex扩展到多变量预测，支持未来已知协变量。它采用双向时间混合器和非对称分组注意力变体混合器，保持严格因果性。在GIFT-Eval和fev-bench上取得零样本SOTA性能。相比Transformer，流式处理时每块计算成本恒定，无需全历史重算。模型单变量模式有38.4M活跃参数，多变量模式额外激活44.1M参数。
TiRex-2: Generalizing TiRex to Multivariate Data and Streaming
We introduce TiRex-2, a recurrent xLSTM-based time series foundation model that generalizes the univariate TiRex to multivariate forecasting with both past and future covariates. Real-world forecasting is inherently sequential: observations arrive continuously, variables evolve jointly, and a subset of covariates is known ahead of time. Existing Transformer-based time series foundation models capture cross-variate dependencies but incur quadratic complexity in context length and require full-history recomputation as new observations arrive. TiRex-2 addresses these limitations through a memory-centric recurrent design that operates at constant per-patch cost under streaming. The model combines a bidirectional time mixer with an asymmetric grouped-attention variate mixer, enabling the integration of future-known covariates while preserving strict causality over target variables. To our knowledge, this is the first time series foundation model that achieves this combination of properties. To support scalable multivariate pretraining, we propose a synthetic coupling pipeline that composes diverse multivariate samples on the fly from large univariate corpora. Empirically, TiRex-2 achieves state-of-the-art zero-shot performance on GIFT-Eval and fev-bench, remains stable when streamed to arbitrary context lengths, and maintains constant inference cost per patch. The model uses 38.4M active parameters in univariate mode, with an additional 44.1M parameters activated for multivariate forecasting.