Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Overview

Autoformer (NeurIPS 2021)

Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Time series forecasting is a critical demand for real applications. Enlighted by the classic time series analysis and stochastic process theory, we propose the Autoformer as a general series forecasting model [paper]. Autoformer goes beyond the Transformer family and achieves the series-wise connection for the first time.

In long-term forecasting, Autoformer achieves SOTA, with a 38% relative improvement on six benchmarks, covering five practical applications: energy, traffic, economics, weather and disease.

Autoformer vs. Transformers

1. Deep decomposition architecture

We renovate the Transformer as a deep decomposition architecture, which can progressively decompose the trend and seasonal components during the forecasting process.



Figure 1. Overall architecture of Autoformer.

2. Series-wise Auto-Correlation mechanism

Inspired by the stochastic process theory, we design the Auto-Correlation mechanism, which can discover period-based dependencies and aggregate the information at the series level. This empowers the model with inherent log-linear complexity. This series-wise connection contrasts clearly from the previous self-attention family.



Figure 2. Auto-Correlation mechansim.

Get Started

  1. Install Python 3.6, PyTorch 1.9.0.
  2. Download data. You can obtain all the six benchmarks from Tsinghua Cloud or Google Drive. All the datasets are well pre-processed and can be used easily.
  3. Train the model. We provide the experiment scripts of all benchmarks under the folder ./scripts. You can reproduce the experiment results by:
bash ./scripts/ETT_script/Autoformer_ETTm1.sh
bash ./scripts/ECL_script/Autoformer.sh
bash ./scripts/Exchange_script/Autoformer.sh
bash ./scripts/Traffic_script/Autoformer.sh
bash ./scripts/Weather_script/Autoformer.sh
bash ./scripts/ILI_script/Autoformer.sh
  1. Sepcial-designed implementation
  • Speedup Auto-Correlation: We built the Auto-Correlation mechanism as a batch-normalization-style block to make it more memory-access friendly. See the paper for details.

  • Without the position embedding: Since the series-wise connection will inherently keep the sequential information, Autoformer does not need the position embedding, which is different from Transformers.

Main Results

We experiment on six benchmarks, covering five main-stream applications. We compare our model with ten baselines, including Informer, N-BEATS, etc. Generally, for the long-term forecasting setting, Autoformer achieves SOTA, with a 38% relative improvement over previous baselines.

Citation

If you find this repo useful, please cite our paper.

@inproceedings{wu2021autoformer,
  title={Autoformer: Decomposition Transformers with {Auto-Correlation} for Long-Term Series Forecasting},
  author={Haixu Wu and Jiehui Xu and Jianmin Wang and Mingsheng Long},
  booktitle={Advances in Neural Information Processing Systems},
  year={2021}
}

Contact

If you have any question or want to use the code, please contact [email protected] .

Acknowledgement

We appreciate the following github repos a lot for their valuable code base or datasets:

https://github.com/zhouhaoyi/Informer2020

https://github.com/zhouhaoyi/ETDataset

https://github.com/laiguokun/multivariate-time-series-data

Owner
THUML @ Tsinghua University
Machine Learning Group, School of Software, Tsinghua University
THUML @ Tsinghua University
The PyTorch implementation for paper "Neural Texture Extraction and Distribution for Controllable Person Image Synthesis" (CVPR2022 Oral)

ArXiv | Get Start Neural-Texture-Extraction-Distribution The PyTorch implementation for our paper "Neural Texture Extraction and Distribution for Cont

Ren Yurui 111 Dec 10, 2022
A Lightweight Experiment & Resource Monitoring Tool 📺

Lightweight Experiment & Resource Monitoring 📺 "Did I already run this experiment before? How many resources are currently available on my cluster?"

170 Dec 28, 2022
Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021)

Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021) Authors: Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song Link to pap

Xinshi Chen 2 Dec 20, 2021
Tello Drone Trajectory Tracking

With this library you can track the trajectory of your tello drone or swarm of drones in real time.

Kamran Asgarov 2 Oct 12, 2022
Flow is a computational framework for deep RL and control experiments for traffic microsimulation.

Flow Flow is a computational framework for deep RL and control experiments for traffic microsimulation. See our website for more information on the ap

867 Jan 02, 2023
Neural Message Passing for Computer Vision

Neural Message Passing for Quantum Chemistry Implementation of different models of Neural Networks on graphs as explained in the article proposed by G

Pau Riba 310 Nov 07, 2022
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable.

Diffrax Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. Diffrax is a JAX-based library providing numerical differe

Patrick Kidger 717 Jan 09, 2023
Rethinking Nearest Neighbors for Visual Classification

Rethinking Nearest Neighbors for Visual Classification arXiv Environment settings Check out scripts/env_setup.sh Setup data Download the following fin

Menglin Jia 29 Oct 11, 2022
An executor that loads ONNX models and embeds documents using the ONNX runtime.

ONNXEncoder An executor that loads ONNX models and embeds documents using the ONNX runtime. Usage via Docker image (recommended) from jina import Flow

Jina AI 2 Mar 15, 2022
Categorizing comments on YouTube into different categories.

Youtube Comments Categorization This repo is for categorizing comments on a youtube video into different categories. negative (grievances, complaints,

Rhitik 5 Nov 26, 2022
TreeSubstitutionCipher - Encryption system based on trees and substitution

Tree Substitution Cipher Generation Algorithm: Generate random tree. Tree nodes

stepa 1 Jan 08, 2022
Generative Models for Graph-Based Protein Design

Graph-Based Protein Design This repo contains code for Generative Models for Graph-Based Protein Design by John Ingraham, Vikas Garg, Regina Barzilay

John Ingraham 159 Dec 15, 2022
E-Ink Magic Calendar that automatically syncs to Google Calendar and runs off a battery powered Raspberry Pi Zero

MagInkCal This repo contains the code needed to drive an E-Ink Magic Calendar that uses a battery powered (PiSugar2) Raspberry Pi Zero WH to retrieve

2.8k Dec 28, 2022
Real-time VIBE: Frame by Frame Inference of VIBE (Video Inference for Human Body Pose and Shape Estimation)

Real-time VIBE Inference VIBE frame-by-frame. Overview This is a frame-by-frame inference fork of VIBE at [https://github.com/mkocabas/VIBE]. Usage: i

23 Jul 02, 2022
Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19)

Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19) Tianyu Wang*, Xin Yang*, Ke Xu, Shaozhe Chen, Qiang Zhang, Ry

Steve Wong 177 Dec 01, 2022
Pytorch implementation of set transformer

set_transformer Official PyTorch implementation of the paper Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks .

Juho Lee 410 Jan 06, 2023
PyTorch implementation of: Michieli U. and Zanuttigh P., "Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations", CVPR 2021.

Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations This is the official PyTorch implementation

Multimedia Technology and Telecommunication Lab 42 Nov 09, 2022
A quantum game modeling of pandemic (QHack 2022)

Contributors: @JongheumJung, @YoonjaeChung, @GyunghunKim Abstract In the regime of a global pandemic, leaders around the world need to consider variou

Yoonjae Chung 8 Apr 03, 2022
Code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization,

FSRA This repository contains the dataset link and the code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV

Dmmm 32 Dec 18, 2022
Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

HamasKhan 3 Jul 08, 2022