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Hi @marah othman, can you provide more details? What model and you running and which hyperparameters? how long is your data?

the model is Automlp the size of my data is 2522 hours the horizan is 630 and the huperparameters is "input_size_multiplier": [1, 2, 3, 4, 5], "h": None, "hidden_size": tune.choice([256, 512, 1024]), "num_layers": tune.randint(2, 6), "learning_rate": tune.loguniform(1e-4, 1e-1), "scaler_type": tune.choice([None, "robust", "standard"]), "max_steps": tune.choice([500, 1000]), "batch_size": tune.choice([16, 32, 64, 128]), "windows_batch_size": tune.choice([64, 128, 256, 512]), "loss": None, "check_val_every_n_epoch": tune.choice([100]), "random_seed": tune.randint(1, 20), **TRAINER_CONF, }

try reducing the input_size_multiplier, it is larger than your data. Keep only 1 and 2

because we multiple the input size by the horizon right ?

yes, input_size = multiplier*horizon

the size of my data doesnt fixed how can i put condition to avoid this problem for futur because i am trying to make the code automatic

If you are specifying your own config, you also need to drop the

input_size_multiplier

and change it to

input_size

directly. You could have if conditions before defining the config dictionary

okay

try to have input size a lot smaller than your data. because models operate based on windows of size input_size+h, if it is too large then there will be very few windows, which is not good

in which operator we use the window size

the model does it internally, it is part of the

BaseWindows

and

BaseRecurrent

base classes, in the

create_windows

method

could please explain a little more

this is general for all models, not for the

auto

part to select hyperparameters. Windows-based models are functions from past values (

input_size

) to future values (

horizon

). During training, models sample a batch of windows (of size

input_size+h

) starting at random timestamps. They produce the forecasts for all windows of the batch, compute training loss (based on the last

h

points of each window), and update parameters.

does the fit function choose the validation set size and test set size to choose the best collection from hyperparams or it doesnt have test set , is my question clear ??

models are evaluated on the validation set. You can specify it will the

val_size

parameter of the

fit

and

cross_validation

methods. If you dont specify

val_size

, auto models will use a validation set equal to 1 horizon of length. The test set is specified by the user, and it is only used for the

cross_validation

function. The models is NOT automatically evaluated in the test set, instead, the function will return the forecasts for the test set so you can visualize/evaluate them.

and also i have this in auto model every time i run the auto model i get different result why i didn't get the same predictions for my variable , do i should get same prediction or not ,and this difference, is it because of random seed and i set the random seed to fixed value but still get different prediction every time i run the auto model