https://arxiv.org/abs/2310.06625

Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2310.06625
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv logo
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Machine Learning

arXiv:2310.06625 (cs)
[Submitted on 10 Oct 2023]

Title:iTransformer: Inverted Transformers Are Effective for Time
Series Forecasting

Authors:Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang,
Lintao Ma, Mingsheng Long
Download a PDF of the paper titled iTransformer: Inverted
Transformers Are Effective for Time Series Forecasting, by Yong Liu
and 6 other authors
Download PDF

    Abstract:The recent boom of linear forecasting models questions
    the ongoing passion for architectural modifications of
    Transformer-based forecasters. These forecasters leverage
    Transformers to model the global dependencies over temporal
    tokens of time series, with each token formed by multiple
    variates of the same timestamp. However, Transformer is
    challenged in forecasting series with larger lookback windows due
    to performance degradation and computation explosion. Besides,
    the unified embedding for each temporal token fuses multiple
    variates with potentially unaligned timestamps and distinct
    physical measurements, which may fail in learning variate-centric
    representations and result in meaningless attention maps. In this
    work, we reflect on the competent duties of Transformer
    components and repurpose the Transformer architecture without any
    adaptation on the basic components. We propose iTransformer that
    simply inverts the duties of the attention mechanism and the
    feed-forward network. Specifically, the time points of individual
    series are embedded into variate tokens which are utilized by the
    attention mechanism to capture multivariate correlations;
    meanwhile, the feed-forward network is applied for each variate
    token to learn nonlinear representations. The iTransformer model
    achieves consistent state-of-the-art on several real-world
    datasets, which further empowers the Transformer family with
    promoted performance, generalization ability across different
    variates, and better utilization of arbitrary lookback windows,
    making it a nice alternative as the fundamental backbone of time
    series forecasting.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2310.06625 [cs.LG]
          (or arXiv:2310.06625v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2310.06625
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Yong Liu [view email]
[v1] Tue, 10 Oct 2023 13:44:09 UTC (4,053 KB)
Full-text links:

Access Paper:

    Download a PDF of the paper titled iTransformer: Inverted
    Transformers Are Effective for Time Series Forecasting, by Yong
    Liu and 6 other authors
  * Download PDF
  * PostScript
  * Other Formats

[by-nc-nd-4]
Current browse context:
cs.LG
< prev   |   next >
new | recent | 2310
Change to browse by:
cs

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

a export BibTeX citation Loading...

BibTeX formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Reddit logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
[ ] Litmaps Toggle
Litmaps (What is Litmaps?)
[ ] scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
( ) Code, Data, Media

Code, Data and Media Associated with this Article

[ ] Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
[ ] DagsHub Toggle
DagsHub (What is DagsHub?)
[ ] Links to Code Toggle
Papers with Code (What is Papers with Code?)
[ ] ScienceCast Toggle
ScienceCast (What is ScienceCast?)
( ) Demos

Demos

[ ] Replicate Toggle
Replicate (What is Replicate?)
[ ] Spaces Toggle
Hugging Face Spaces (What is Spaces?)
( ) Related Papers

Recommenders and Search Tools

[ ] Link to Influence Flower
Influence Flower (What are Influence Flowers?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)
[ ] IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)

  * Author
  * Venue
  * Institution
  * Topic

( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack