Forecast combination integrates information from various sources by consolidating multiple forecast results from the target time series. Instead of the need to select a single optimal forecasting model, this paper introduces a deep learning ensemble forecasting model based on the Dirichlet process. Initially, the learning rate is sampled with three basis distributions as hyperparameters to convert the infinite mixture into a finite one. All checkpoints are collected to establish a deep learning sub-model pool, and weight adjustment and diversity strategies are developed during the combination process. The main advantage of this method is its ability to generate the required base learners through a single training process, utilizing the decaying strategy to tackle the challenge posed by the stochastic nature of gradient descent in determining the optimal learning rate. To ensure the method's generalizability and competitiveness, this paper conducts an empirical analysis using the weekly dataset from the M4 competition and explores sensitivity to the number of models to be combined. The results demonstrate that the ensemble model proposed offers substantial improvements in prediction accuracy and stability compared to a single benchmark model.

翻译：预测组合通过整合目标时间序列的多个预测结果来融合来自不同来源的信息。本文不再需要选择单一最优预测模型，而是引入了一种基于狄利克雷过程的深度学习集成预测模型。首先，采用三种基础分布对学习率进行采样作为超参数，将无限混合转化为有限混合。收集所有检查点以建立深度学习子模型池，并在组合过程中开发权重调整和多样性策略。该方法的主要优势在于能够通过单次训练过程生成所需的基础学习器，并利用衰减策略应对梯度下降随机性在确定最优学习率时带来的挑战。为确保方法的泛化性和竞争力，本文使用M4竞赛的周数据集进行实证分析，并探究对组合模型数量的敏感性。结果表明，与单个基准模型相比，本文提出的集成模型在预测精度和稳定性方面均取得了显著提升。