Hi
I’d like to predict n variables, indexed on date, instead of one.
So, I have daily sales volume of n products, and multiple categorical and continuous variables, created from date, like day of week etc., values taken from Google trends, weather, etc.
I’d like to build a model to predict all sales volumes in one go.
How to do it using fastai tabular?
You can pass in a list of columns for dep_var and it will do exactly what you want 
Thanks!
Do they have to be in cont_vars as well?
No! You actually want to leave them out of cont_vars as cont and cat vars are your independent variables
Cool thank you!!
No problem! Your domain sounds very similar to the Rossmann problem back in part one, that notebook can get you started as well
I did study Rossmann, but the prediction part is still hard for me. So far, I concentrated on data cleaning and building a model. Now, I must learn how to actually use the model.
@tomdraug did you get this working? The docs refer to dep_var as a str type, not list, for TabularDataBunch.from_df.
Hi @jc849
Yes, it works
I get all variables at once.
self.data = (TabularList.from_df(self.df_train_valid, path=’.’,
cat_names=self.cat_vars, cont_names=self.cont_vars,
procs=self.procs)
.split_by_idx(range(len(self.df_train_valid)-self.VALID_SIZE,len(self.df_train_valid)))
.label_from_df(cols=self.dep_vars, label_cls=FloatList)
.add_test(TabularList.from_df(self.test_df))
.databunch(bs=64))
Thanks, that’s great!
@tomdraug Could you share your notebook? I’m working on a similar project and struggling to pull multiple dependent variables
I will send you next week