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
Code repo for realtime multi-person pose estimation in CVPR'17 (Oral)

Realtime Multi-Person Pose Estimation By Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh. Introduction Code repo for winning 2016 MSCOCO Keypoints Cha

Zhe Cao 4.9k Dec 31, 2022
An end-to-end PyTorch framework for image and video classification

What's New: March 2021: Added RegNetZ models November 2020: Vision Transformers now available, with training recipes! 2020-11-20: Classy Vision v0.5 R

Facebook Research 1.5k Dec 31, 2022
(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

IsoTree Fast and multi-threaded implementation of Extended Isolation Forest, Fair-Cut Forest, SCiForest (a.k.a. Split-Criterion iForest), and regular

141 Dec 29, 2022
Off-policy continuous control in PyTorch, with RDPG, RTD3 & RSAC

arXiv technical report soon available. we are updating the readme to be as comprehensive as possible Please ask any questions in Issues, thanks. Intro

Zhihan 31 Dec 30, 2022
Reproduce partial features of DeePMD-kit using PyTorch.

DeePMD-kit on PyTorch For better understand DeePMD-kit, we implement its partial features using PyTorch and expose interface consuing descriptors. Tec

Shaochen Shi 8 Dec 17, 2022
A pytorch implementation of faster RCNN detection framework (Use detectron2, it's a masterpiece)

Notice(2019.11.2) This repo was built back two years ago when there were no pytorch detection implementation that can achieve reasonable performance.

Ruotian(RT) Luo 1.8k Jan 01, 2023
DANA paper supplementary materials

DANA Supplements This repository stores the data, results, and R scripts to generate these reuslts and figures for the corresponding paper Depth Norma

0 Dec 17, 2021
Self-Guided Contrastive Learning for BERT Sentence Representations

Self-Guided Contrastive Learning for BERT Sentence Representations This repository is dedicated for releasing the implementation of the models utilize

Taeuk Kim 16 Dec 04, 2022
A PyTorch-based open-source framework that provides methods for improving the weakly annotated data and allows researchers to efficiently develop and compare their own methods.

Knodle (Knowledge-supervised Deep Learning Framework) - a new framework for weak supervision with neural networks. It provides a modularization for se

93 Nov 06, 2022
Learning Representations that Support Robust Transfer of Predictors

Transfer Risk Minimization (TRM) Code for Learning Representations that Support Robust Transfer of Predictors Prepare the Datasets Preprocess the Scen

Yilun Xu 15 Dec 07, 2022
DANet for Tabular data classification/ regression.

Deep Abstract Networks A pyTorch implementation for AAAI-2022 paper DANets: Deep Abstract Networks for Tabular Data Classification and Regression. Bri

Ronnie Rocket 55 Sep 14, 2022
Simple converter for deploying Stable-Baselines3 model to TFLite and/or Coral

Running SB3 developed agents on TFLite or Coral Introduction I've been using Stable-Baselines3 to train agents against some custom Gyms, some of which

Gary Briggs 16 Oct 11, 2022
NAACL2021 - COIL Contextualized Lexical Retriever

COIL Repo for our NAACL paper, COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List. The code covers learning

Luyu Gao 108 Dec 31, 2022
[NeurIPS 2021] Code for Unsupervised Learning of Compositional Energy Concepts

Unsupervised Learning of Compositional Energy Concepts This is the pytorch code for the paper Unsupervised Learning of Compositional Energy Concepts.

45 Nov 30, 2022
PyTorch Implementation of our paper Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation

PyTorch Implementation of our paper Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation

Zechen Bai 12 Jul 08, 2022
An official implementation of the Anchor DETR.

Anchor DETR: Query Design for Transformer-Based Detector Introduction This repository is an official implementation of the Anchor DETR. We encode the

MEGVII Research 276 Dec 28, 2022
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

Zhao Jian 3.1k Jan 04, 2023
Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations Requirements The code is implemented in Python and requires

1 Nov 03, 2021
This repository provides some of the code implemented and the data used for the work proposed in "A Cluster-Based Trip Prediction Graph Neural Network Model for Bike Sharing Systems".

cluster-link-prediction This repository provides some of the code implemented and the data used for the work proposed in "A Cluster-Based Trip Predict

Bárbara 0 Dec 28, 2022
Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks

Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks arXiv preprint: https://arxiv.org/abs/2201.02143. Architec

19 Nov 30, 2022