arXiv:2607.01204v1 Announce Type: new Abstract: 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
The continuous development of AI models for time series forecasting, especially with streaming data, is a critical area of active research, driven by the need for more efficient and accurate real-world applications.
This development represents a significant step towards more computationally efficient and adaptable foundation models for multivariate time series forecasting, which is crucial for various industries reliant on continuous data streams.
Foundation models for time series forecasting are evolving from static, full-history recomputation to more dynamic, streaming-compatible architectures, allowing for better handling of real-time data.
- · AI/ML researchers
- · Companies with streaming data operations
- · Cloud providers
- · Financial and logistics sectors
- · Legacy time series forecasting methods
- · Transformer-based models with quadratic complexity
Improved accuracy and efficiency in predicting complex, multivariate systems across various domains.
Accelerated development of autonomous AI systems and agents that rely on real-time, context-aware forecasts.
Enhanced automation and optimization across critical infrastructure, from supply chains to energy grids, potentially impacting global economic efficiency.
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Read at arXiv cs.LG