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| TiRex：基于 xLSTM 的零样本时间序列预测模型× | 长短期记忆网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2025 | 1997 |
| 提出者≠ | NX-AI (xLSTM team) | Hochreiter, S. & Schmidhuber, J. |
| 类型≠ | Pretrained zero-shot time-series forecasting model | Recurrent neural network (gated memory cell) |
| 开创性文献≠ | Auer, A., Podest, P., Klotz, D., Böck, S., Klambauer, G., & Hochreiter, S. (2025). TiRex: Zero-shot forecasting across long and short horizons with enhanced in-context learning. arXiv preprint. link ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| 别名 | Time-series xLSTM Forecaster, TiRex Zero-Shot, xLSTM Time-Series Model, Zaman Serisi Sıfır-Atım Tahmincisi | LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells |
| 相关≠ | 3 | 5 |
| 摘要≠ | TiRex is a pretrained zero-shot time-series forecasting model introduced in 2025 by the NX-AI xLSTM team (Auer et al.). Built on the Extended Long Short-Term Memory (xLSTM) architecture, TiRex is trained at scale on diverse time-series corpora and can forecast unseen datasets without any fine-tuning. Its core idea is to exploit enhanced in-context learning: the model reads the entire available history as a context and produces forecasts for both short and long horizons directly from that context. | LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps. |
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