[TiDE] Exception("No windows available for training")

[rjguedes8]

I'm using TiDE and for some combination of hyper parameters I face the same issue.
My series have a length >= input size + h, any idea why does this happen? I cannot find anything common between the hyper parameters that failed that might be triggering this exception...

these do not work:

{"input_size": 12, "h": 13, "num_encoder_layers": 18, "num_decoder_layers": 11, "decoder_output_dim": 16, "hidden_size": 19, "temporal_width": 20, "temporal_decoder_dim": 64, "dropout": 0.2201250055472177, "batch_size": 16, "max_steps": 2887, "random_seed": 42, "learning_rate": 0.0036755406235537713, "layernorm": false, "scaler_type": "standard", "num_lr_decays": 7, "val_check_steps": 100, "start_padding_enabled": true}
{"input_size": 12, "h": 13, "num_encoder_layers": 3, "num_decoder_layers": 6, "decoder_output_dim": 6, "hidden_size": 8, "temporal_width": 31, "temporal_decoder_dim": 32, "dropout": 0.167877884832288, "batch_size": 32, "max_steps": 1493, "random_seed": 42, "learning_rate": 0.0012947352550811552, "layernorm": false, "scaler_type": "minmax", "num_lr_decays": 7, "val_check_steps": 50, "start_padding_enabled": true}
{"input_size": 26, "h": 13, "num_encoder_layers": 5, "num_decoder_layers": 7, "decoder_output_dim": 26, "hidden_size": 14, "temporal_width": 1, "temporal_decoder_dim": 64, "dropout": 0.2447828048783552, "batch_size": 32, "max_steps": 1183, "random_seed": 42, "learning_rate": 0.012868199705960067, "layernorm": false, "scaler_type": "standard", "num_lr_decays": 7, "val_check_steps": 50, "start_padding_enabled": true}
{"input_size": 12, "h": 13, "num_encoder_layers": 30, "num_decoder_layers": 27, "decoder_output_dim": 26, "hidden_size": 28, "temporal_width": 6, "temporal_decoder_dim": 16, "dropout": 0.22650625477678882, "batch_size": 32, "max_steps": 2261, "random_seed": 42, "learning_rate": 0.04951797346057748, "layernorm": false, "scaler_type": "robust", "num_lr_decays": 3, "val_check_steps": 100, "start_padding_enabled": true}

this works:

{"input_size": 52, "h": 13, "num_encoder_layers": 9, "num_decoder_layers": 6, "decoder_output_dim": 10, "hidden_size": 24, "temporal_width": 13, "temporal_decoder_dim": 32, "dropout": 0.21459933827060712, "batch_size": 32, "max_steps": 1121, "random_seed": 42, "learning_rate": 0.007506012394677122, "layernorm": false, "scaler_type": "standard", "num_lr_decays": 3, "val_check_steps": 50, "start_padding_enabled": true}

[elephaint]

Hi, thanks for using NeuralForecast!

I don't see the logic of failing just by looking at the hyperparameters, do you have a piece of code that I can run that generates the error?

[rjguedes8]

I'm going to close the issue, I tried to replicate the error but couldn't, even using the same data and code.