40 个门店的需求预测：该训练 40 个独立模型还是一个全局模型？
Best approach for demand forecasting across 40 locations? Separate models or one global model? [p]
一名 B.Tech CSE（AI/ML）学生在多智能体供应链优化项目中，就约 110 万条、覆盖 40 个地点的合成零售数据，询问需求预测应训练 40 个按地点独立的模型，还是训练一个带地点标识与地点特征的全局模型。他还担心全局模型会把区域差异平均化，并询问分组混合方案、合成数据下的评估注意事项，以及如何避免多地点、时间相关特征带来的数据泄漏。
Hey everyone,
I'm a final-year B.Tech CSE (AI/ML) student working on a multi-agent supply chain optimization project. I'm currently focusing on the Demand Forecasting Agent and would appreciate some advice on model selection and training strategy.
We're working with a synthetic retail supply chain dataset containing approximately 1.1 million records across 40 different locations.
The goal is to predict product-level demand for each location as accurately as possible, so the forecasts can later be used by other agents for inventory planning and supply chain decisions.
I have a few questions:
- Global vs local models: Since the dataset contains 40 locations, should I train 40 separate models, one for each location, or a single global model trained on data from all 40 locations simultaneously, using location identifiers and location-specific features?
- Location-specific patterns: If I use a global model, how effectively can it learn differences in demand patterns between locations? I'm concerned that combining all locations might cause the model to average out regional patterns and produce inaccurate location-level forecasts.
- Alternative approaches: Would a hybrid approach, such as grouping similar locations or using a global model with location-specific adjustments, be worth exploring?
- Synthetic data: Since the dataset is synthetic, are there particular precautions I should take when evaluating model performance and generalization? How can I ensure the model is learning meaningful demand patterns rather than artifacts of the data-generation process?
I'd also appreciate suggestions on which forecasting models or architectures would be most suitable for this problem, along with the appropriate validation strategy, especially how to avoid data leakage when dealing with multiple locations and time-dependent features.
My priority is accurate product-level, location-specific demand forecasting, rather than simply achieving good aggregate accuracy across the entire dataset.
Would love to hear what approaches you'd recommend and why. Thanks!
submitted by /u/Crafty_Rabbit2411
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来源：r/MachineLearning · reddit.com