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.

翻译：金融科技的发展见证了深度学习模型在预测消费者行为方面的广泛应用，这一趋势在提升信贷策略和增强市场效率方面展现出显著潜力。本研究基于UCI在线零售II数据集，对电子商务交易进行多时间范围需求预测。与先前版本混合金融贷款叙事与零售数据的做法不同，本文专注于零售市场行为，并明确定义预测目标：针对H=1、7、14时间范围的每日每SKU需求（或收入）。我们提出了一种混合序列模型，该模型结合了多尺度时间卷积、门控循环模块和时间感知自注意力机制。模型采用标准回归损失函数进行训练，并通过MAE、RMSE、sMAPE、MASE和Theil's U_2指标进行评估，采用严格的时间划分以避免数据泄露。我们将模型与ARIMA/Prophet、LSTM/GRU、LightGBM以及前沿的Transformer预测模型（TFT、Informer、Autoformer、N-BEATS）进行基准比较。结果显示，模型在峰值/节假日期间实现了持续的精度提升和更强的鲁棒性。我们进一步提供了消融实验和统计显著性检验以确保改进的可靠性，并公开了实现细节以促进可复现性。