用混合模型预测电商商品日需求，提升长周期预测精度。
Optimization of Deep Learning Models for Dynamic Market Behavior Prediction
- 融合多尺度卷积、门控循环与时间感知注意力的混合序列模型。
- 在H=1/7/14天预测上，MAE和RMSE均优于传统方法与SOTA Transformer模型。
- 适用于电商零售需求预测，尤其在节假日高峰期表现更稳健。
金融科技发展推动深度学习在预判消费者行为中的应用，显著提升了信贷策略与市场效率。本文基于UCI Online Retail II数据集，聚焦电商零售行为，明确预测目标为单品（SKU）每日需求或收入，预测时序跨度H=1、7、14天。提出一种混合序列模型，结合多尺度时间卷积、门控循环模块与时间感知自注意力机制，采用标准回归损失训练，并通过严格的时序划分避免信息泄露，以MAE、RMSE、sMAPE、MASE及Theil's U_2进行评估。对比ARIMA/Prophet、LSTM/GRU、LightGBM及TFT、Informer、Autoformer、N-BEATS等先进Transformer模型，结果表明该模型在多时段预测中持续取得精度提升，尤其在高峰与节假日期间鲁棒性更强。通过消融实验与统计显著性检验验证改进可靠性，并公开实现细节以促进可复现性。
原文摘要 · Abstract (English)
The advent of financial technology has witnessed a surge in the utilization of deep learning models to anticipate consumer conduct, a trend that has demonstrated considerable potential in enhancing lending strategies and bolstering market efficiency. We study multi-horizon demand forecasting on e-commerce transactions using the UCI Online Retail II dataset. Unlike prior versions of this manuscript that mixed financial-loan narratives with retail data, we focus exclusively on retail market behavior and define a clear prediction target: per SKU daily demand (or revenue) for horizons H=1,7,14. We present a hybrid sequence model that combines multi-scale temporal convolutions, a gated recurrent module, and time-aware self-attention. The model is trained with standard regression losses and evaluated under MAE, RMSE, sMAPE, MASE, and Theil's U_2 with strict time-based splits to prevent leakage. We benchmark against ARIMA/Prophet, LSTM/GRU, LightGBM, and state-of-the-art Transformer forecasters (TFT, Informer, Autoformer, N-BEATS). Results show consistent accuracy gains and improved robustness on peak/holiday periods. We further provide ablations and statistical significance tests to ensure the reliability of improvements, and we release implementation details to facilitate reproducibility.
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