如有侵权或其他问题欢迎留言联系更正或删除。目录1. Introduction2. Method3. 实验结果出处AAAI 2023代码链接cure-lab/LTSF-Linear: [AAAI-23 Oral] Official implementation of the paper Are Transformers Effective for Time Series Forecasting?1. Introduction(1) time series forecasting (TSF)(2) 回顾 “ Transformer (Vaswani et al. 2017) ” 的各领域优秀表现(3) IMS vs. DMS :→ Consequently, IMS forecasting is preferable when there is a highly-accurate single-step forecaster, and T is relatively small. In contrast, DMS forecasting generates more accurate predictions when it is hard to obtain an unbiased single-step forecasting model, or T is large.→ IMS 有 更小的方差 (smaller variance)但更受 误差累积效应 (error accumulation effects) 影响.tips: Not all time series are predictable, let alone long-term forecasting (e.g., for chaotic systems). We hypothesize that long-term forecasting is only feasible for those time series with a relatively clear trend and periodicity.(4) 历史工作导图2. Method2.1 基础模型LTSF - Linear(1) 建模公式(2) 注意LTSF - Linear 在不同变量间共享权重且不对任何空间相关性建模2.2 改进模型DLinear *本部分解读源于DLinearAre Transformers Effective for Time Series Forecasting? - 知乎2.3NLinearMeanwhile, to boost the performance of LTSF-Linear when there is a distribution shift in the dataset, NLinear first subtracts the input by the last value of the sequence. Then, the input goes through a linear layer, and the subtracted part is added back before making the final prediction. The subtraction and addition in NLinear are a simple normalization for the input sequence.3. 实验结果4. 有用的信息Transformer 对时序顺序信息 捕获能力不足不抗噪