针对预测任务，LEAF动态基准填补了多维事件评估空白，让大模型预测能力测试更贴近现实。
arXiv:2605.16358v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly applied to forecasting. To evaluate this capability whil…
重新审视线性映射在长期时间序列预测中的有效性，挑战了复杂模型的必要地位。
arXiv:2305.10721v2 Announce Type: replace Abstract: Introduction: Long-term time series forecasting (LTSF) has gained significant attention in recent …
提出L-Drive方法，突破单映射限制，用潜在上下文驱动时间序列预测，为时序建模提供新思路。
arXiv:2605.17730v1 Announce Type: new Abstract: Mainstream methods for multivariate time-series forecasting largely follow the Direct-Mapping paradigm…
从动力学视角提出PRO-DYN，挑战Transformer同质化，直接学习过去到未来的映射。
arXiv:2507.15774v3 Announce Type: replace-cross Abstract: While deep learning is facing an homogenization across modalities led by Transformers, they …