Predicting parallel time series with multiple features
Here are two ways to model the problem. The first one is simpler, the second one is more complex but closer to your original statement of the problem.

Store as an input feature

You can consider the store as a feature to pass to your LSTM. With two different stores, just add a binary input feature "store" where store A is 0 and store B is 1, for example.

Then, you can implement it this way.

from keras.models import Sequential
from keras.layers import LSTM, Dense

timesteps = 20
n_features = 5
model = Sequential()
# add +1 to n_features for the store identifier
model.add(LSTM(32, input_shape=(timesteps,n_features + 1), return_sequences=True))
model.add(LSTM(32))
model.add(Dense(1,activation="relu"))
model.compile(optimizer="rmsprop", loss="mse")


One sample of your data at a given timestep will be a vector of the form (feature_1, feature_2, ..., feature_n, store).

Multivariate time-series prediction

Here we input both time series and aim to predict next values of both stores. So you have a shared-LSTM processing store separately, then concatentate both produced embeddings, and compute the predicted values.

from keras.models import Model
from keras.layers import LSTM, Dense, Concatenate, Input

timesteps = 20
n_features = 5

# Store A and B time-series inputs
a_inputs = Input(shape=(timesteps, n_features))
b_inputs = Input(shape=(timesteps, n_features))

# Stacked LSTM
lstm_1 = LSTM(32, return_sequences=True)
lstm_2 = LSTM(32)
# Stacked LSTM on Store A 
a_embedding = lstm_1(a_inputs)
a_embedding = lstm_2(a_embedding)
# Stacked LSTM on Store B
b_embedding = lstm_1(b_inputs)
b_embedding = lstm_2(b_embedding)

# Concatenate embeddings and define model
outputs = Concatenate()([a_embedding, b_embedding])
outputs = Dense(64)(outputs)
outputs = Dense(2, activation="relu")(outputs)
model = Model([a_inputs, b_inputs], outputs)
model.compile(optimizer="rmsprop", loss="mse")


Inspired by section 7.1.5 Shared weight sharing, in Deep Learning with Python by F. Chollet