Switch-Hurdle：一种结合AR障碍解码器的MoE编码器用于间歇性需求预测
Switch-Hurdle: A MoE Encoder with AR Hurdle Decoder for Intermittent Demand Forecasting
AI总结：
Switch-Hurdle通过结合MoE编码器和AR障碍解码器，有效捕捉间歇性需求的双重过程，实现高精度预测。
摘要：
间歇性需求，一种以长序列零销售值穿插着偶发非零值为特征的模式，在零售和供应链预测中持续构成挑战。传统方法如ARIMA、指数平滑或Croston变体以及现代神经架构如DeepAR和基于Transformer的模型在处理此类数据时往往表现不佳，因为它们将需求视为单一连续过程或在扩展到许多稀疏序列时变得计算成本高昂。为了解决这些限制，我们引入了Switch-Hurdle：一种新的框架，结合了混合专家（MoE）编码器和基于障碍的概率解码器。编码器在正向传递过程中使用稀疏的Top-1专家路由，但在反向传递过程中通过直通估计器（STE）近似密集。解码器采用交叉注意力自回归设计，具有共享的障碍头，明确将预测任务分为两个组成部分：一个二分类组件用于估计销售的概率，以及一个条件回归组件用于在销售发生时预测数量。这种结构化的分离使模型能够捕捉到间歇性需求固有的发生和数量过程。在M5基准和一个大型专有零售数据集上的实验证明，Switch-Hurdle在保持可扩展性的同时实现了最先进的预测性能。
英文摘要：
Intermittent demand, a pattern characterized by long sequences of zero sales punctuated by sporadic, non-zero values, poses a persistent challenge in retail and supply chain forecasting. Both traditional methods, such as ARIMA, exponential smoothing, or Croston variants, as well as modern neural architectures such as DeepAR and Transformer-based models often underperform on such data, as they treat demand as a single continuous process or become computationally expensive when scaled across many sparse series. To address these limitations, we introduce Switch-Hurdle: a new framework that integrates a Mixture-of-Experts (MoE) encoder with a Hurdle-based probabilistic decoder. The encoder uses a sparse Top-1 expert routing during the forward pass yet approximately dense in the backward pass via a straight-through estimator (STE). The decoder follows a cross-attention autoregressive design with a shared hurdle head that explicitly separates the forecasting task into two components: a binary classification component estimating the probability of a sale, and a conditional regression component, predicting the quantity given a sale. This structured separation enables the model to capture both occurrence and magnitude processes inherent to intermittent demand. Empirical results on the M5 benchmark and a large proprietary retail dataset show that Switch-Hurdle achieves state-of-the-art prediction performance while maintaining scalability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
