Optimization of Deep Learning Models for Dynamic Market Behavior Prediction

• URL: http://arxiv.org/abs/2511.19090v1
• Date: Mon, 24 Nov 2025 13:30:52 GMT
• Title: Optimization of Deep Learning Models for Dynamic Market Behavior Prediction
• Authors: Shenghan Zhao, Yuzhen Lin, Ximeng Yang, Qiaochu Lu, Haozhong Xue, Gaozhe Jiang,
• Abstract summary: We study multi-horizon demand forecasting on e-commerce transactions using the UCI Online Retail II dataset.<n>We present a hybrid sequence model that combines multi-scale temporal convolutions, a gated recurrent module, and time-aware self-attention.<n>Results show consistent accuracy gains and improved on peak/holiday periods.
• Score: 4.594360512414794
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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