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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
by asah 2y ago
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.
?
- vdvsvwvwvwvwv 2y agoLgtm; A NN is literally a probability distribution producer.
- tech_ken 2y ago> 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