Half wayish through the seminar 2

mon15thsept/mon15thsept.md

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+AI for Demand prediction in Retail and E-Commerce 
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+This group is talking about using AI methods for demand prediction in the physical retain and e-commerce setting. They survey 7 papers that cover statistical methods, classical machine learning methods and modern deep learning methods. The older statistical methods are used as baselines and provide much clearer interpretations and understanding of the assumptions to the model user. The move to modern DL methods have allowed the model makers to the opportunity to give more varied input information (weather, economy, etc) to these models as opposed to just simple sale history. It does seem from this survey that classical machine learning methods are more prominent (and effective?) then modern DL architectures. Interestingly it is hybrid systems that outperform others in many of the studies, these hybrid systems are either ensemble in nature or segment the problem into multiple steps. I would of thought that given the scale of the data available some novel DL architecture could be constructed to maximally use both the data and compute power that is now available. I find the concept of the GNNs to be of most interest, specifically as it could relate to private federated learning to allow various companies "share insight" to maximize efficiency.
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+Seven papers; supermarket demand, dl packaged good predictions, survey where modern is better, comparing trees and lstm, clustering prior to regression, spatial based GNNs, comparing models on walmart data
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+Questions:
+- Are the models working by predicting the amount of demand for a specific product in a time period. Are many of these models trying to predict individual customers purchasing habits?
+- In the paper comparing different models on the Walmart data it seems to me that the performance gains are trivially small, am I missing something? 
+- In any of the papers were these models deployed to be used by the actual companies that would need this sort of data?
+- It seems to me that this time series data would suit itself well to continual learning or even stream learning (for short term predictions) is this idea used in the demand prediction field?