Choice of LSTM for price prediction

Question

I have a dataset with features (f) for different stocks (S) and want to infer for price using an LSTM model. Here is my df:

year	S1_price	S1_f1	S1_f2	S2_price	S2_f1	S2_f2	Sn_price	Sn_f1	Sn_f2
2010	100	0.1	0.12	200	0.2	0.22	300	0.3	0.32
2011	105	0.4	0.42	205	0.5	0.52	305	0.6	0.62
2012	110	0.7	0.72	210	0.8	0.82	310	0.9	0.92
n

and so on... (example values).

I would like to predict the prices of every stock by using the features as inputs looking 1yr back into the past.

Example for Stock 1 (predict 2012):

[[0.1 0.12]
 [0.4 0.42]] 110

However, I want to to that for all stocks, so I am not sure which type of LSTM to use.

Your help would be very much appreciated!

Answer 1

I tend to like GRU and its variants for this type of problem. Although, if your dataset isn't too large you can test some toy models(smaller versions) of the different types to know which will achieve the best accuracy.

Typically a good size to start with in that case would be about 20-25% of your full model size.