针对稀疏和低幅时间序列的预测难题，提出抗偏差新模型SPADE-S。
SPADE-S: A Sparsity-Robust Foundational Forecaster
- 设计抗稀疏与低幅偏差的架构，改进损失函数与训练策略
- 在三个真实数据集上实现最高15%的精度提升，分位数预测增益显著
- 适合高维、异构的电商需求预测场景，尤其关注长尾商品
尽管时间序列预测取得显著进展，但对幅度差异大或稀疏模式强的时间序列建模仍是当前深度学习模型的挑战。本文指出现有模型在低幅和稀疏序列上系统性表现不佳的原因：损失函数对高幅序列存在隐式偏倚、训练采样方法不当，以及时间序列编码能力有限。为此提出SPADE-S，一种鲁棒的预测架构，显著降低幅度与稀疏性带来的系统性偏差，提升整体预测精度。实证结果表明，SPADE-S在来自大型在线零售商的三组不同使用场景中，优于现有最先进方法。具体而言，根据分位数预测和序列幅度不同，准确率最高可提升15%；在三个数据集上，分别实现P90预测准确率提升2.21%、6.58%、4.28%，P50提升0.92%、0.77%、1.95%，数据规模覆盖300万至7亿条序列。
原文摘要 · Abstract (English)
Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging for state-of-the-art deep learning architectures. We identify several factors that lead existing models to systematically underperform on low-magnitude and sparse time series, including loss functions with implicit biases toward high-magnitude series, training-time sampling methods, and limitations of time series encoding methods. SPADE-S is a robust forecasting architecture that significantly reduces magnitude- and sparsity-based systematic biases and improves overall prediction accuracy. Empirical results demonstrate that SPADE-S outperforms existing state-of-the-art approaches across a diverse set of use cases in demand forecasting. In particular, we show that, depending on the quantile forecast and magnitude of the series, SPADE-S can improve forecast accuracy by up to 15%. This results in P90 overall forecast accuracy gains of 2.21%, 6.58%, and 4.28%, and P50 forecast accuracy gains of 0.92%, 0.77%, and 1.95%, respectively, for each of three distinct datasets, ranging from 3 million to 700 million series, from a large online retailer.
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