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Hi Chris! Yes, TFT is an intrinsically slow model. We analyzed the cost of each component and the model itself accounts for almost all the computation (not the loader). For each batch it has to unroll the LSTMs and then it has the attention layer, with quadratic cost on the size of the window).

There are many hyperparameters you can tweak to reduce the cost/time:
• Reduce `windows_batch_size`  (less windows per batch)
• Reduce `input_size`  (shorter input window)
• Reduce `hidden_size`  (smaller model)

If you have a validation set (set `val_size&gt;0`), you can also set `early_stop_patience_steps` larger than 0, to stop the training if the validation loss is not improving.

Hey Cristian, thanks for running this to ground. We'll try these suggestions and let you know how it goes. lol TFT looks like a snail next to TCN :smile:

It is, TFT is a WindowsBased approach
TCN uses the forking sequences optimization

They have a huge tradeoff between GPU memory and computational speed

If we have some time we can develop a Transformer based algorithm that uses forking sequences