  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.
