Implementation of ETSformer, state of the art time-series Transformer, in Pytorch

Hi! I was wondering if you have thought about aggregating seasonal and growth latents for additional tasks (for example classification)? What are the possible ways to bring latents into single feature vector in your opinion? The easiest one would be just get the mean along layers and time dimensions but that seams to be too naive. Another idea I had it to use Cross Attention mechanic with single time query key to aggregate latents:

all_latents = torch.cat([latent_growths, latent_seasonals], dim=-1)
all_latents = rearrange(all_latents, 'b n l d -> (b l) n d')
# q = nn.Parameter(torch.randn(all_latents_dim))
q = repeat(q, 'd -> b 1 d', b = all_latents.shape[0])
agg_latent = cross_attention(query=q, context=all_latents)
agg_latent = rearrange(all_latents, '(b l) n d -> b (l n) d')
agg_latent = agg_latent.mean(dim=1) # may be we should have done it before cross attention?

Would be great to hear your thoughts

https://github.com/lucidrains/ETSformer-pytorch/blob/2561053007e919409b3255eb1d0852c68799d24f/etsformer_pytorch/etsformer_pytorch.py#L440

In my early tests I see some instability in training results, I was wondering if it might be good idea to LayerNorm latents before constructing key and values?

https://github.com/lucidrains/ETSformer-pytorch/blob/e1d8514b44d113ead523aa6307986833e68eecc5/etsformer_pytorch/etsformer_pytorch.py#L233-L235

It looks like you are not using growth and growth_smoothing_weightsto calculate growth_term

Hello,

i was trying to run the provided class and see following error: Function ScatterBackward0 returned an invalid gradient at index 1 - got [64, 4, 128] but expected shape compatible with [64, 33, 128]

model = ETSFormer(
            time_features = 9,
            model_dim = 128,
            embed_kernel_size = 3,
            layers = 2,
            heads = 4,
            K = 4,
            dropout = 0.2
        )

input = torch.rand(64, 64, 9) x = model(input, num_steps_forecast = 16)

@lucidrains Thanks for making the code of the model available!

In your paper, you state that the model infers seasonal patterns itself, so that there is no need to add time features like week, month, etc.

Still, to increase the applicability of your approach, does the current implementation allow to add any (time-invariant and time-varying) features, e.g., categorical or numeric?

https://github.com/lucidrains/ETSformer-pytorch/blob/2e0d465576c15fc8d84c4673f93fdd71d45b799c/etsformer_pytorch/etsformer_pytorch.py#L327

you pass latents on wrong order to Level module: according to forward method first should be growth and then seasonal

Hello,

I was trying to run the ETSformer for ETT dataset. The paper mentions that the dataset is split as 60/20/20 for train, validation and test. Could you give some insight as to how the dataset split is happening in the code.

Thank you.