I'm working on a time series with 8 features, where at the beginning of each day,
features 1 to 5are unknown,
features 6 to 8are known, and
features 1, 2are the targets to be predicted.
Currently, I'm using 3-lag time steps, so the columns of the data matrix looks like this:
As far as I understand, the shape of the input of an
LSTM layer should be
(num_rows, num_lags, num_features). However, this shape does not fit my data matrix. My current idea is to use two branches where the first one takes historical data with shape
(num_rows, 3, 8) as input and the second one takes data of time
t with shape
(num_rows, 3) as input. Then, I'll feed the first branch into
LSTM and the second branch into
Dense and concatenate them later on.
I'm wondering whether my idea is feasible. If not, what are the common methods to deal with this situtation?