Pascal Schindler
05/09/2023, 9:29 AMIntegerGreaterThan(lower_bound=0)
with the following config: What are the reasosns?
horizon = 60
config_nhits = {
"input_size": tune.choice([14 ,28, 28*2, 28*3, 28*5, 2 * horizon]), # Length of input window
"n_blocks": 5*[1], # Length of input window
"mlp_units": 5 * [[512, 512]], # Length of input window
"interpolation_mode": tune.choice(['linear']),
"n_pool_kernel_size": tune.choice([5*[1], 5*[2], 5*[4],
[8, 4, 2, 1, 1], [16, 8, 1]]), # MaxPooling Kernel size
"n_freq_downsample": tune.choice([[8, 4, 2, 1, 1],
[1, 1, 1, 1, 1],
[168, 24, 1],
[24, 12, 1],
[1, 1, 1]]), # Interpolation expressivity ratios
"learning_rate": tune.loguniform(1e-4, 1e-2), # Initial Learning rate
"scaler_type": tune.choice([None]), # Scaler type
"max_steps": tune.choice([1000]), # Max number of training iterations
"batch_size": tune.choice([16, 32, 64, 128, 256, 512]), # Number of series in batch
"windows_batch_size": tune.choice([32, 64, 128, 256, 512, 1024, 2048]), # Number of windows in batch
"random_seed": tune.randint(1, 20),
"scaler_type": tune.choice(["robust", None]), # Random seed
"hist_exog_list": ["week_day", "month", "trends"],
"futr_exog_list": ["week_day", "month"]
}
Cristian (Nixtla)
05/09/2023, 2:21 PMscaler_type
two times
2. If you want to use 5 stacks, you should also modify stack_types
to 5*["identity"]
3. Some n_freq_downsample
entries have 3 components, they should all have 5 (it has to match number of stacks).