方法对比
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| TiRex：基于 xLSTM 的零样本时间序列预测模型× | TimesFM：面向时间序列预测的仅解码器基础模型× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2025 | 2024 |
| 提出者≠ | NX-AI (xLSTM team) | Abhimanyu Das et al. (Google) |
| 类型≠ | Pretrained zero-shot time-series forecasting model | Pre-trained decoder-only transformer for zero-shot time-series forecasting |
| 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 ↗ | Das, A., Kong, W., Sen, R., & Zhou, Y. (2024). A decoder-only foundation model for time-series forecasting. ICML. link ↗ |
| 别名 | Time-series xLSTM Forecaster, TiRex Zero-Shot, xLSTM Time-Series Model, Zaman Serisi Sıfır-Atım Tahmincisi | Time-series Foundation Model, Google TimesFM, TimesFM forecaster, Zaman Serisi Temel Modeli |
| 相关 | 3 | 3 |
| 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. | TimesFM is a pre-trained foundation model for univariate time-series forecasting introduced by Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou from Google in 2024. The model adopts a decoder-only transformer architecture, similar in spirit to large language models, and is trained on a large corpus of real-world and synthetic time-series data. Its central innovation is the ability to perform accurate zero-shot forecasting across diverse domains without task-specific fine-tuning. |
| ScholarGate数据集 ↗ |
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