This study proposes novel sales forecasting approaches that merge deep learning methods in a hybrid model. Long short-term memory (LSTM) is adopted for modeling the temporal characteristics of the data, whereas the convolutional neural network (CNN) focuses on identifying and extracting relevant exogenous information. We propose stacked (S-CNN-LSTM) and parallel (P-CNN-LSTM) hybrid architectures to understand complex time series data with varying seasonal patterns and multiple products correlations. The performance drivers of both architectures were empirically tested with a real-world multivariate retail dataset and outperformed when compared with simple neural network architectures and standard autoregressive methods for short and long-term forecasting horizons. When compared with traditional predictive approaches, the proposed hybrid models reduce the computational complexity while providing flexibility and robustness.

中文翻译：

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用于零售销售预测的混合卷积长短期记忆模型

这项研究提出了新颖的销售预测方法，将深度学习方法合并到混合模型中。采用长短期记忆（LSTM）对数据的时间特征进行建模，而卷积神经网络（CNN）则侧重于识别和提取相关的外源信息。我们提出堆叠 (S-CNN-LSTM) 和并行 (P-CNN-LSTM) 混合架构来理解具有不同季节模式和多种产品相关性的复杂时间序列数据。这两种架构的性能驱动因素都通过现实世界的多元零售数据集进行了实证测试，并且与简单的神经网络架构和短期和长期预测范围的标准自回归方法相比，其性能优于。与传统的预测方法相比，所提出的混合模型降低了计算复杂性，同时提供了灵活性和鲁棒性。