How to predict past test set on time series data using LSTM I'm trying to do a regression on some inventory amounts with the following model using Keras:

model = Sequential()
model.add(LSTM(100, batch_input_shape=(BATCH_SIZE, TIME_STEPS, 
X_train_timeseries.shape[2]), dropout=0.0, 
           recurrent_dropout=0.0, stateful=True, 
kernel_initializer='random_uniform'))
model.add(Dropout(rate=0.5))
model.add(Dense(20, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
optimizer = optimizers.RMSprop(lr=LEARNING_RATE)
model.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['mae'])


I have a time step of 60, how do I predict past my test data to make actual predictions into the future?
The best answer I found is here

Unfortunately I couldn't get it to work for my solution but reworked my solution and just incremented the sliding window, appended that answer to my test data, and iterated like that.