论文标题
想法：时间序列预测的可解释动态整体体系结构
IDEA: Interpretable Dynamic Ensemble Architecture for Time Series Prediction
论文作者
论文摘要
我们通过即时可以解释的集合来提高单变量时间序列点预测的准确性和概括。我们提出了一个可解释的动态集成体系结构（思想），其中可解释的基础学习者以稀疏的沟通作为一个小组独立地提供了预测。该模型由几个通过组背景残差和经常性输入竞争连接的几个顺序堆叠组组成。由端到端训练驱动的合奏，无论是水平和垂直训练带来了最新的（SOTA）表演。预测准确性比旅游数据集的最佳统计基准提高了2.6％，而M4数据集上的最佳深度学习基准则提高了2％。该体系结构具有多个优势，适用于来自各个域的时间序列，可以向具有专门模块化结构的用户解释，并且对任务分布的变化进行了鲁棒。
We enhance the accuracy and generalization of univariate time series point prediction by an explainable ensemble on the fly. We propose an Interpretable Dynamic Ensemble Architecture (IDEA), in which interpretable base learners give predictions independently with sparse communication as a group. The model is composed of several sequentially stacked groups connected by group backcast residuals and recurrent input competition. Ensemble driven by end-to-end training both horizontally and vertically brings state-of-the-art (SOTA) performances. Forecast accuracy improves by 2.6% over the best statistical benchmark on the TOURISM dataset and 2% over the best deep learning benchmark on the M4 dataset. The architecture enjoys several advantages, being applicable to time series from various domains, explainable to users with specialized modular structure and robust to changes in task distribution.
