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

Related tags

Deep Learningdeep-learningtime-seriestransformersartificial-intelligenceexponential-smoothing
Overview

ETSformer - Pytorch

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

Install

$ pip install etsformer-pytorch

Usage

import torch
from etsformer_pytorch import ETSFormer

model = ETSFormer(
    time_features = 4,
    model_dim = 512,                # in paper they use 512
    embed_kernel_size = 3,          # kernel size for 1d conv for input embedding
    layers = 2,                     # number of encoder and corresponding decoder layers
    heads = 8,                      # number of exponential smoothing attention heads
    K = 4,                          # num frequencies with highest amplitude to keep (attend to)
    dropout = 0.2                   # dropout (in paper they did 0.2)
)

timeseries = torch.randn(1, 1024, 4)

pred = model(timeseries, num_steps_forecast = 32) # (1, 32, 4) - (batch, num steps forecast, num time features)

For using ETSFormer for classification, using cross attention pooling on all latents and level output

import torch
from etsformer_pytorch import ETSFormer, ClassificationWrapper

etsformer = ETSFormer(
    time_features = 1,
    model_dim = 512,
    embed_kernel_size = 3,
    layers = 2,
    heads = 8,
    K = 4,
    dropout = 0.2
)

adapter = ClassificationWrapper(
    etsformer = etsformer,
    dim_head = 32,
    heads = 16,
    dropout = 0.2,
    level_kernel_size = 5,
    num_classes = 10
)

timeseries = torch.randn(1, 1024)

logits = adapter(timeseries) # (1, 10)

Citation

@misc{woo2022etsformer,
    title   = {ETSformer: Exponential Smoothing Transformers for Time-series Forecasting}, 
    author  = {Gerald Woo and Chenghao Liu and Doyen Sahoo and Akshat Kumar and Steven Hoi},
    year    = {2022},
    eprint  = {2202.01381},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}
Comments
  • What are your thoughts on using latents for additional classification task

    What are your thoughts on using latents for additional classification task

    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

    opened by inspirit 15
  • Pre LayerNorm might be required for k,v?

    Pre LayerNorm might be required for k,v?

    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?

    opened by inspirit 5
  • growth_term calculation error

    growth_term calculation error

    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

    opened by inspirit 4
  • Backward gradient error

    Backward gradient error

    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)

    opened by inspirit 3
  • Does ETS-Former allow adding features

    Does ETS-Former allow adding features

    @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?

    opened by StatMixedML 2
  • wrong order of arguments

    wrong order of arguments

    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

    opened by inspirit 1
  • Clarification regarding data pre-processing

    Clarification regarding data pre-processing

    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.

    opened by vageeshmaiya 2
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Phil Wang
Working with Attention. It's all we need
Phil Wang
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