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I don't have the details of your exact use case, but I cannot imagine any complex non-linearities involved in your sales process. If ARIMA models produce decent

by xgb84j 9y ago

I don't have the details of your exact use case, but I cannot imagine any complex non-linearities involved in your sales process. If ARIMA models produce decent results for your use case I would try to improve the ARIMA model through additional data, rather than switching to deep learning. If you are convinced that there are complex non-linearities that an ARIMA model cannot describe, then I would try to use RNNs to find a pattern in your ARIMA models' residuals and try to augment your ARIMA model with an RNN.

nonbel 9y ago
>"I cannot imagine any complex non-linearities involved in your sales process" Knowing nothing about his sales process and features, I would assume there are many complex non-linearities (to be possibly leveraged for better predictions). I find this statement bizarre.
- xgb84j 9y ago
  What I mean by this statement is that there are lots of "tricks" such as interaction terms, regime switching and non-linear transformation of features to handle non-linearities in linear models (e.g. different food sold before Christmas). But if you can give me an example of a non-linearity in sales forecasting that cannot be fit by a linear model but can by an RNN I'd honestly be really interested in that.
ganeshkrishnan 9y ago
> If ARIMA models produce decent results for your use case I would try to improve the ARIMA model through additional data, rather than switching to deep learning. ARIMA models cannot take into consideration special scenarios or even multivariate features (Like location and time). It's good for simple forecasting but when you need more "human like" predictions we are betting on neural networks. For example we would instinctively know that sales of turkey goes up on Thanksgiving. ARIMA model cannot factor this but RNN/LSTM should be able to (Theoretically speaking).
- xgb84j 9y ago
  There are techniques regime switching and indicator functions that can handle exactly this type of interaction in a linear model. https://en.wikipedia.org/wiki/Dummy_variable_(statistics) https://en.wikipedia.org/wiki/Dummy_variable_(statistics) https://en.wikipedia.org/wiki/Interaction_(statistics) https://en.wikipedia.org/wiki/Interaction_(statistics)

by xgb84j 9y ago

nonbel 9y ago

>"I cannot imagine any complex non-linearities involved in your sales process" Knowing nothing about his sales process and features, I would assume there are many complex non-linearities (to be possibly leveraged for better predictions). I find this statement bizarre.

xgb84j 9y ago
What I mean by this statement is that there are lots of "tricks" such as interaction terms, regime switching and non-linear transformation of features to handle non-linearities in linear models (e.g. different food sold before Christmas). But if you can give me an example of a non-linearity in sales forecasting that cannot be fit by a linear model but can by an RNN I'd honestly be really interested in that.

ganeshkrishnan 9y ago

> If ARIMA models produce decent results for your use case I would try to improve the ARIMA model through additional data, rather than switching to deep learning. ARIMA models cannot take into consideration special scenarios or even multivariate features (Like location and time). It's good for simple forecasting but when you need more "human like" predictions we are betting on neural networks. For example we would instinctively know that sales of turkey goes up on Thanksgiving. ARIMA model cannot factor this but RNN/LSTM should be able to (Theoretically speaking).

xgb84j 9y ago
There are techniques regime switching and indicator functions that can handle exactly this type of interaction in a linear model. https://en.wikipedia.org/wiki/Dummy_variable_(statistics) https://en.wikipedia.org/wiki/Dummy_variable_(statistics) https://en.wikipedia.org/wiki/Interaction_(statistics) https://en.wikipedia.org/wiki/Interaction_(statistics)