Standard LSTM(Long Short-Term Memory) neural networks provide accurate predictions for sales data in the retail industry, but require a lot of computing power. It can be challenging especially for mid to small retail industries. This paper examines LSTM model compression by gradually reducing the number of hidden units from 128 to 16. We used the Kaggle Store Item Demand Forecasting dataset, which has 913,000 daily sales records from 10 stores and 50 items, to look at the trade-off between model size and how accurate the predictions are. Experiments show that lowering the number of hidden LSTM units to 64 maintains the same level of accuracy while also improving it. The mean absolute percentage error (MAPE) ranges from 23.6% for the full 128-unit model to 12.4% for the 64-unit model. The optimized model is 73% smaller (from 280KB to 76KB) and 47% more accurate. These results show that larger models do not always achieve better results.

翻译：标准LSTM（长短期记忆）神经网络可为零售业销售数据提供精准预测，但其计算资源需求较高，对中小型零售企业而言尤为困难。本研究通过将隐藏单元数量从128逐步减少至16，系统探讨了LSTM模型压缩方法。我们采用Kaggle商店商品需求预测数据集（包含10家商店、50种商品的913,000条日销售记录），深入分析了模型规模与预测精度之间的权衡关系。实验表明：将LSTM隐藏单元降至64时，模型在保持原有精度的同时实现了性能提升——完整128单元模型的平均绝对百分比误差（MAPE）为23.6%，而64单元模型降至12.4%。优化后的模型体积缩小73%（从280KB减至76KB），精度提升47%。这些结果表明：更大规模的模型并不总能获得更优性能。