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
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

512x512 flowers after 12 hours of training, 1 gpu 256x256 flowers after 12 hours of training, 1 gpu Pizza 'Lightweight' GAN Implementation of 'lightwe

Phil Wang 1.5k Jan 02, 2023
Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series

Clairvoyance: A Pipeline Toolkit for Medical Time Series Authors: van der Schaar Lab This repository contains implementations of Clairvoyance: A Pipel

van_der_Schaar \LAB 89 Dec 07, 2022
Video Frame Interpolation with Transformer (CVPR2022)

VFIformer Official PyTorch implementation of our CVPR2022 paper Video Frame Interpolation with Transformer Dependencies python = 3.8 pytorch = 1.8.0

DV Lab 63 Dec 16, 2022
A dual benchmarking study of visual forgery and visual forensics techniques

A dual benchmarking study of facial forgery and facial forensics In recent years, visual forgery has reached a level of sophistication that humans can

8 Jul 06, 2022
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching

RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching This repository contains the source code for our paper: RAFT-Stereo: Multilevel

Princeton Vision & Learning Lab 328 Jan 09, 2023
PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition

PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition The unofficial code of CDistNet. Now, we ha

25 Jul 20, 2022
Ros2-voiceroid2 - ROS2 wrapper package of VOICEROID2

ros2_voiceroid2 ROS2 wrapper package of VOICEROID2 Windows Only Installation Ins

Nkyoku 1 Jan 23, 2022
Code release for SLIP Self-supervision meets Language-Image Pre-training

SLIP: Self-supervision meets Language-Image Pre-training What you can find in this repo: Pre-trained models (with ViT-Small, Base, Large) and code to

Meta Research 621 Dec 31, 2022
An efficient PyTorch implementation of the winning entry of the 2017 VQA Challenge.

Bottom-Up and Top-Down Attention for Visual Question Answering An efficient PyTorch implementation of the winning entry of the 2017 VQA Challenge. The

Hengyuan Hu 731 Jan 03, 2023
Stacs-ci - A set of modules to enable integration of STACS with commonly used CI / CD systems

Static Token And Credential Scanner CI Integrations What is it? STACS is a YARA

STACS 18 Aug 04, 2022
This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning].

CG3 This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning]. R

12 Oct 28, 2022
Benchmark library for high-dimensional HPO of black-box models based on Weighted Lasso regression

LassoBench LassoBench is a library for high-dimensional hyperparameter optimization benchmarks based on Weighted Lasso regression. Note: LassoBench is

Kenan Šehić 5 Mar 15, 2022
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling

large-scale-ITE-UM-benchmark This repository contains code and data to reproduce the results of the paper "A Large Scale Benchmark for Individual Trea

10 Nov 19, 2022
(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework

(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework Background: Outlier detection (OD) is a key data mining task for identify

Yue Zhao 127 Jan 05, 2023
Toward Spatially Unbiased Generative Models (ICCV 2021)

Toward Spatially Unbiased Generative Models Implementation of Toward Spatially Unbiased Generative Models (ICCV 2021) Overview Recent image generation

Jooyoung Choi 88 Dec 01, 2022
Source code for "Pack Together: Entity and Relation Extraction with Levitated Marker"

PL-Marker Source code for Pack Together: Entity and Relation Extraction with Levitated Marker. Quick links Overview Setup Install Dependencies Data Pr

THUNLP 173 Dec 30, 2022
NHS AI Lab Skunkworks project: Long Stayer Risk Stratification

NHS AI Lab Skunkworks project: Long Stayer Risk Stratification A pilot project for the NHS AI Lab Skunkworks team, Long Stayer Risk Stratification use

NHSX 21 Nov 14, 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations

HIVE: Evaluating the Human Interpretability of Visual Explanations Project Page | Paper This repo provides the code for HIVE, a human evaluation frame

Princeton Visual AI Lab 16 Dec 13, 2022
A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical Reasoning

Orchard Dataset This repository contains the code used for generating the Orchard Dataset, as seen in the Multi-Hierarchical Reasoning in Sequences: S

Bill Pung 1 Jun 05, 2022
Reinforcement learning algorithms in RLlib

raylab Reinforcement learning algorithms in RLlib and PyTorch. Installation pip install raylab Quickstart Raylab provides agents and environments to b

Ângelo 50 Sep 08, 2022