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汇集来自不同来源的信息。本文无需选择单一最优预测模型，而是提出一种基于狄利克雷过程的深度学习集成预测模型。首先，以三种基础分布作为超参数对学习率进行采样，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，并在组合过程中制定权重调整和多样性策略。该方法的主要优势在于能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的随机性在确定最优学习率时带来的挑战。为确保方法的泛化能力和竞争力，本文使用M4竞赛的周度数据集进行实证分析，并探讨了对组合模型数量的敏感性。结果表明，与单一基准模型相比，所提出的集成模型在预测准确性和稳定性方面均有显著提升。