提出多尺度时间序列模型，提升能源预测精度并给出可信度估计。
EnergyPatchTST: Multi-scale Time Series Transformers with Uncertainty Estimation for Energy Forecasting
- 通过多尺度特征提取捕捉不同时间粒度的模式
- 误差降低7%-12%，且提供基于蒙特卡洛的不确定性估计
- 适合数据有限的能源场景，尤其适用于带气象变量的预测
精准可靠的能源时间序列预测对电力调度与分配至关重要。当前深度学习方法虽为主流，但面对多尺度动态和真实数据不规则性仍存在局限。为此，我们提出EnergyPatchTST，一种专为能源预测设计的Patch Time Series Transformer扩展模型。其主要创新包括：(1) 多尺度特征提取机制，以捕捉不同时间分辨率下的模式；(2) 概率预测框架，通过蒙特卡洛消除法估算不确定性；(3) 集成未来已知变量（如温度、风速）的输入路径；(4) 利用预训练与微调策略，增强小样本能源数据集的表现。在多个常用能源数据集上的实验表明，EnergyPatchTST优于现有主流方法，预测误差降低7%-12%，同时提供可靠不确定性估计，为能源领域的时间序列预测提供了重要参考。
原文摘要 · Abstract (English)
Accurate and reliable energy time series prediction is of great significance for power generation planning and allocation. At present, deep learning time series prediction has become the mainstream method. However, the multi-scale time dynamics and the irregularity of real data lead to the limitations of the existing methods. Therefore, we propose EnergyPatchTST, which is an extension of the Patch Time Series Transformer specially designed for energy forecasting. The main innovations of our method are as follows: (1) multi-scale feature extraction mechanism to capture patterns with different time resolutions; (2) probability prediction framework to estimate uncertainty through Monte Carlo elimination; (3) integration path of future known variables (such as temperature and wind conditions); And (4) Pre-training and Fine-tuning examples to enhance the performance of limited energy data sets. A series of experiments on common energy data sets show that EnergyPatchTST is superior to other commonly used methods, the prediction error is reduced by 7-12%, and reliable uncertainty estimation is provided, which provides an important reference for time series prediction in the energy field.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。