SPADE模型通过拆分峰值事件与常规预测，提升促销后需求预测准确性。
$\spadesuit$ SPADE $\spadesuit$ Split Peak Attention DEcomposition
- 将峰值事件与常规预测分离建模，用掩码卷积和峰值注意力模块处理
- 在海量商品数据上实现促销后预测提升4.5%，最受影响场景改善30%
- 适合需要精准预测促销/节假日后需求的零售与供应链场景
需求预测面临由促销、节假日等特殊时期引发的峰值事件（PEs）挑战。这些事件导致需求突然飙升，随后进入需求回落期。现有神经网络如MQCNN和MQT会因过度响应峰值，将高需求延续至后续的峰后事件（PPE）期间，造成严重预测偏差。为此，我们提出一种新型神经预测模型Split Peak Attention DEcomposition（SPADE）。该模型通过将预测任务拆分为峰值事件建模与非峰值建模两部分，并采用掩码卷积滤波器与专用峰值注意力模块，有效降低峰值事件对后续预测的影响。我们在涵盖数亿商品的世界级零售数据集上验证了SPADE性能：整体PPE预测误差降低4.5%，促销与节假日后最受影响的预测场景改善30%，峰值事件自身预测精度提升3.9%（相较当前生产模型）。
原文摘要 · Abstract (English)
Demand forecasting faces challenges induced by Peak Events (PEs) corresponding to special periods such as promotions and holidays. Peak events create significant spikes in demand followed by demand ramp down periods. Neural networks like MQCNN and MQT overreact to demand peaks by carrying over the elevated PE demand into subsequent Post-Peak-Event (PPE) periods, resulting in significantly over-biased forecasts. To tackle this challenge, we introduce a neural forecasting model called Split Peak Attention DEcomposition, SPADE. This model reduces the impact of PEs on subsequent forecasts by modeling forecasting as consisting of two separate tasks: one for PEs; and the other for the rest. Its architecture then uses masked convolution filters and a specialized Peak Attention module. We show SPADE's performance on a worldwide retail dataset with hundreds of millions of products. Our results reveal an overall PPE improvement of 4.5%, a 30% improvement for most affected forecasts after promotions and holidays, and an improvement in PE accuracy by 3.9%, relative to current production models.
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