RNN time-series predictions with multiple features containing non-numeric features and numeric features? The question RNN&#39;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.

So 

How can I predict time series with multiple features contain numeric features and non-numeric features?



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Possible ways to expose these categorical variables as part of a time-step to be fed to an RNN include producing One_Hot Tensors, Hash Table Representations, or Embedding Layer Outputs from the categorical fields and concatenating all of the feature tensors at each time-step to feed to your RNN. 

If, instead of discrete categories, you're looking to interpret the strings that make up the fields as sequences themselves, you could encode them numerically at a word- or character level and pass them through a subsidiary RNN to produce the internal representation at each timestep. This would be appropriate only for cases where you have a very large variety among strings in those fields.

Apologies for the Tensorflow links if you're not using that framework.