Hello, I came across your interesting paper highlighting the potential of mixLSTM in handling time-varying relationships. I was trying to examine how well mixLSTM handles a one-time shift in the following data:
x1 = np.random.normal(5,1,1000)
x2 = np.random.normal(2,5,1000)
y_base = 10+(3*x1[:600]*x1[:600])+(5*x2[:600])
y_shifted = 10+(6.2*x1[600:]*x1[600:])+(5*x2[600:])
y = np.concatenate((y_base,y_shifted))
For this (X,y), I was experimenting with:
args= {'d':2,'hidden_size':300,'output_size':1,'batch_size':32,'T':30 and above}
k is 2 by default.
I tried various values of the above parameters but the model was not able to detect the shift. Could you pls. suggest guidelines on setting T and k?
Hello, I came across your interesting paper highlighting the potential of mixLSTM in handling time-varying relationships. I was trying to examine how well mixLSTM handles a one-time shift in the following data:
For this (X,y), I was experimenting with:
args= {'d':2,'hidden_size':300,'output_size':1,'batch_size':32,'T':30 and above}
k is 2 by default.
I tried various values of the above parameters but the model was not able to detect the shift. Could you pls. suggest guidelines on setting T and k?