How to feed a 4D tensor into LSTM model?
The solution I came up with is to make the whole data in each day to be a long long long sequence.
So the dimension will be 1D, and can be fed into the LSTM model.
But I don't think this is the optimal solution.
Does anyone come up with better answer?
Appreciate.
I want to use an LSTM model to predict the future sales.
The data is like the table below.




date      
store
family    
sales




01/01/2013
1    
AUTOMOTIVE
0    


01/01/2013
1    
BABY CARE  
0    


01/01/2013
1    
BEAUTY    
1    


..        
.    
..        
.    


01/01/2013
2    
AUTOMOTIVE
0    


01/01/2013
2    
BABY CARE  
0    


..        
.    
..        
.    


01/01/2013
50    
AUTOMOTIVE
0    


..        
.    
..        
.    


01/02/2013
1    
AUTOMOTIVE
0    


01/02/2013
1    
BABY CARE  
50    


..        
.    
..        
.    


01/02/2013
2    
AUTOMOTIVE
500  


01/02/2013
2    
BABY CARE  
0    


..        
.    
..        
.    


01/02/2013
50    
AUTOMOTIVE
0    


..        
.    
..        
.    


..        
.    
..        
.    


12/31/2015
1    
AUTOMOTIVE
0    


12/31/2015
1    
BABY CARE  
50    


..        
.    
..        
.    


12/31/2015
2    
AUTOMOTIVE
500  


12/31/2015
2    
BABY CARE  
0    


..        
.    
..        
.    


12/31/2015
50    
AUTOMOTIVE
0    


..        
.    
..        
.    





For each day, it has 50 stores.
For each store, it has different type of family (product). (They are all in perfect order, thank God).
Last, for each type of family, it has its sales.

Here is the problem.
The dimension of input of LSTM model is (Batch_Size, Sequence_Length, Input_Dimension). It is a 3D tensor.
However, in my case, my Input_Dimension is 2D, which is (rows x columns)
rows: number of rows in one day, which is 1782
columns: number of features, which is 2 (store and family)
Is there a good way to make my data into a shape which can be fed into a LSTM model?
Thanks a lot!