I can't help but wonder if real systems have additional (perhaps subtle) signals, which can be provided to a neural network, which then outperforms these simple algorithms.
For example, customers arrive at the grocery store in clusters due to traffic lights, schools getting out, etc. Even without direct signals, a NN could potentially pickup on these "rules" given other inputs, e.g. time of day, weather, etc.
> For example, customers arrive at the grocery store in clusters due to traffic lights, schools getting out, etc.
You're kind of just describing seasonality components and exogenous regressors; RNNs do actually function quite well for demand forecasting of this type but even simple models (Holt-Winters or a Bayesian state space model or something) can be really effective
For example, customers arrive at the grocery store in clusters due to traffic lights, schools getting out, etc. Even without direct signals, a NN could potentially pickup on these "rules" given other inputs, e.g. time of day, weather, etc.
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