The question RNN's with multiple features is ambiguous and not explicitly in differentiating different features. I want to understand how to use RNN to predict time-series with multiple features containing non-numeric data as well. As a deep learning model, I assume I don't need to quantify the non-numeric elements.
Suppose sales data
Sales | Weather | Holiday | Temperature 100 | Windy | Yes | 3 2000 | Sunny | Yes | 20 200 | Sunny | No | 30 -5 | Stormy | No | 3 -50 | Cold | No | -50 500 | Cold | Yes | -20
where I want to predict the
Sales column with the other columns. I have found demos such as here about using RNN with numeric data, without enriching the data with non-numeric data.
How can I predict time series with multiple features contain numeric features and non-numeric features?
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