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
Owner
Phil Wang
Working with Attention. It's all we need
Phil Wang
GitHub Repository
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)

[NeurIPS 2021 Spotlight] HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning [Paper] This is Official PyTorch implementatio

42 Nov 01, 2022
Supporting code for short YouTube series Neural Networks Demystified.

Neural Networks Demystified Supporting iPython notebooks for the YouTube Series Neural Networks Demystified. I've included formulas, code, and the tex

Stephen 1.3k Dec 23, 2022
A certifiable defense against adversarial examples by training neural networks to be provably robust

DiffAI v3 DiffAI is a system for training neural networks to be provably robust and for proving that they are robust. The system was developed for the

SRI Lab, ETH Zurich 202 Dec 13, 2022
This repository contains project created during the Data Challenge module at London School of Hygiene & Tropical Medicine

LSHTM_RCS This repository contains project created during the Data Challenge module at London School of Hygiene & Tropical Medicine (LSHTM) in collabo

Lukas Kopecky 3 Jan 30, 2022
Acoustic mosquito detection code with Bayesian Neural Networks

HumBugDB Acoustic mosquito detection with Bayesian Neural Networks. Extract audio or features from our large-scale dataset on Zenodo. This repository

31 Nov 28, 2022
This repo generates the training data and the model for Morpheus-Deblend

Morpheus-Deblend This repo generates the training data and the model for Morpheus-Deblend. This is the active development repo for the project and as

Ryan Hausen 2 Apr 18, 2022
Implementation of "Debiasing Item-to-Item Recommendations With Small Annotated Datasets" (RecSys '20)

Debiasing Item-to-Item Recommendations With Small Annotated Datasets This is the code for our RecSys '20 paper. Other materials can be found here: Ful

Microsoft 34 Aug 10, 2022
Multi-tool reverse engineering collaboration solution.

CollaRE v0.3 Intorduction CollareRE is a tool for collaborative reverse engineering that aims to allow teams that do need to use more then one tool du

105 Nov 27, 2022
Expert Finding in Legal Community Question Answering

Expert Finding in Legal Community Question Answering Arian Askari, Suzan Verberne, and Gabriella Pasi. Expert Finding in Legal Community Question Answ

Arian Askari 3 Oct 31, 2022
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation (CVPR 2021)

ASGNet The code is for the paper "Adaptive Prototype Learning and Allocation for Few-Shot Segmentation" (accepted to CVPR 2021) [arxiv] Overview data/

Gen Li 91 Dec 23, 2022
Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"

CSA: Contextual Similarity Aggregation with Self-attention for Visual Re-ranking PyTorch training code for CSA (Contextual Similarity Aggregation). We

Hui Wu 19 Oct 21, 2022
Systematic generalisation with group invariant predictions

Requirements are Python 3, TensorFlow v1.14, Numpy, Scipy, Scikit-Learn, Matplotlib, Pillow, Scikit-Image, h5py, tqdm. Experiments were run on V100 GPUs (16 and 32GB).

Faruk Ahmed 30 Dec 01, 2022
Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker This repository contai

Nikita 12 Dec 14, 2022
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity

[ICLR 2022] Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity by Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elen

VITA 18 Dec 31, 2022
Mini Software that give reminder to drink water as per your weight.

Water Notification Desktop Python The Mini Software built in Python (tkinter) that will remind you to drink water on specific time span based on your

Om Jogani 5 Dec 16, 2022
DeepLab-ResNet rebuilt in TensorFlow

DeepLab-ResNet-TensorFlow This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset. Fr

Vladimir 1.2k Nov 04, 2022
EZ graph is an easy to use AI solution that allows you to make and train your neural networks without a single line of code.

EZ-Graph EZ Graph is a GUI that allows users to make and train neural networks without writing a single line of code. Requirements python 3 pandas num

1 Jul 03, 2022
Machine Learning Time-Series Platform

cesium: Open-Source Platform for Time Series Inference Summary cesium is an open source library that allows users to: extract features from raw time s

632 Dec 26, 2022
Graph parsing approach to structured sentiment analysis.

Fine-grained Sentiment Analysis as Dependency Graph Parsing This repository contains the code and datasets described in following paper: Fine-grained

Jeremy Barnes 36 Dec 12, 2022
《Truly shift-invariant convolutional neural networks》(2021)

Truly shift-invariant convolutional neural networks [Paper] Authors: Anadi Chaman and Ivan Dokmanić Convolutional neural networks were always assumed

Anadi Chaman 46 Dec 19, 2022