Switch-Hurdle: A MoE Encoder with AR Hurdle Decoder for Intermittent Demand Forecasting
Fabian Mu\c{s}at, Simona C\u{a}buz

TL;DR
Switch-Hurdle introduces a novel MoE encoder with a hurdle-based decoder for improved intermittent demand forecasting, effectively modeling sale occurrence and quantity, and demonstrating state-of-the-art results on benchmark datasets.
Contribution
The paper presents a new framework combining MoE encoding with a hurdle-based decoder to better handle the unique challenges of intermittent demand forecasting.
Findings
Achieves state-of-the-art accuracy on M5 benchmark.
Maintains scalability across large sparse datasets.
Effectively models both sale occurrence and amount.
Abstract
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…
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Taxonomy
TopicsForecasting Techniques and Applications · Traffic Prediction and Management Techniques · Customer churn and segmentation
