Forecasting online product sales is essential for retailers and e-commerce platforms, but it can be challenging owing to the complex dynamics and mixed trends in sales data. Popular end-to-end approaches tend to capture spurious correlations in historical data, whereas two-stage approaches that decompose time series and make separate predictions often result in error accumulation. To address these issues, we propose PoissonGP, a novel Bayesian model that employs a non-homogeneous Poisson process with a Gaussian process prior for sales prediction. PoissonGP can capture complex patterns in data with multiple trends and manage distribution shifts caused by changes in long-run sales. Additionally, it incorporates forecast uncertainty and provides interpretability for building efficient and visual decision support systems. Experimental results on several synthetic and empirical datasets suggest that PoissonGP outperforms existing approaches, making it a promising tool for e-commerce platforms.

预测在线产品销售对于零售商和电子商务平台至关重要，但由于销售数据的复杂动态和混合趋势，预测在线产品销售可能具有挑战性。流行的端到端方法往往会捕获历史数据中的虚假相关性，而分解时间序列并进行单独预测的两阶段方法通常会导致错误累积。为了解决这些问题，我们提出了 PoissonGP，一种新颖的贝叶斯模型，采用非齐次泊松过程和高斯过程销售预测之前。PoissonGP 可以捕获具有多种趋势的复杂数据模式，并管理由长期销售变化引起的分布变化。此外，它还纳入了预测不确定性，并为构建高效、可视化的决策支持系统提供了可解释性。几个合成和经验数据集的实验结果表明，PoissonGP 优于现有方法，使其成为电子商务平台的一个有前途的工具。