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 dataSales | Weather | Holiday | Temperature100   | Windy   | Yes     | 32000  | Sunny   | Yes     | 20200   | Sunny   | No      | 30-5    | Stormy  | No      | 3-50   | Cold    | No      | -50500   | Cold    | Yes     | -20where 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?Other interesting questions related to RNNMultivariate time-series RNN -question in SO with numeric dataTime series prediction using ARIMA vs LSTM
RNN time-series predictions with multiple features containing non-numeric features and numeric features?
deep learning;rnn