LSTNet
Running the code
Download & extract the training data:
Train the model (~1.5 hours on Tesla K80 GPU with default hyperparams):
Results & Comparison
The model in the paper predicts with h = 3 on electricity dataset, achieving RSE = 0.0906, RAE = 0.0519 and CORR = 0.9195 on test dataset
This MXNet implementation achieves RSE = 0.0880, RAE = 0.0542 after 100 epochs on the validation dataset
Saved model checkpoint files can be found in models/
Hyperparameters
The default arguements in lstnet.py
achieve equivolent performance to the published results. For other datasets, the following hyperparameters provide a good starting point:
q = {2^0, 2^1, ... , 2^9} (1 week is typical value)
Convolutional num filters = {50, 100, 200}
Convolutional kernel sizes = 6,12,18
Recurrent state size = {50, 100, 200}
Skip recurrent state size = {20, 50, 100}
Skip distance = 24 (tune this based on domain knowledge)
AR lambda = {0.1,1,10}
Adam optimizer LR = 0.001
Dropout after every layer = {0.1, 0.2}
Epochs = 100