iTransformer - Inverted Transformer Are Effective for Time Series Forecasting

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for lucidrains from gravatar.com lucidrains

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Phil Wang
  • Tags artificial intelligence, deep learning, transformers, attention mechanism, time series forecasting
Classifiers

Project description

The author of this package has not provided a project description

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for lucidrains from gravatar.com lucidrains

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Phil Wang
  • Tags artificial intelligence, deep learning, transformers, attention mechanism, time series forecasting
Classifiers

Release history Release notifications | RSS feed

0.6.0

0.5.5

0.5.4

0.5.3

0.5.2

0.5.1

0.5.0

0.4.4

0.4.2

0.4.1

0.4.0

0.3.5

0.3.4

0.3.3

0.3.2

This version

0.3.1

0.3.0

0.2.0

0.1.0

0.0.7

0.0.6

0.0.5

0.0.4

0.0.3

0.0.2

0.0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

iTransformer-0.3.1.tar.gz (9.5 kB view details)

Uploaded Source

Built Distribution

iTransformer-0.3.1-py3-none-any.whl (9.4 kB view details)

Uploaded Python 3

File details

Details for the file iTransformer-0.3.1.tar.gz.

File metadata

  • Download URL: iTransformer-0.3.1.tar.gz
  • Upload date:
  • Size: 9.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for iTransformer-0.3.1.tar.gz
Algorithm Hash digest
SHA256 f688682c6cde53b7b9a3fa4cb049980d818caa6338fc52eec4138b5fa28de033
MD5 308dce5eb6be0d8ebf4a75929745f93a
BLAKE2b-256 688ddca10282795ca2a97c9375d00fe7224c9837fb0e80c3054e141cbbbd0510

See more details on using hashes here.

Provenance

File details

Details for the file iTransformer-0.3.1-py3-none-any.whl.

File metadata

File hashes

Hashes for iTransformer-0.3.1-py3-none-any.whl
Algorithm Hash digest
SHA256 c4b1783c62643e140e96028688587b77344e993db8efbb71c5ba6b6535e228f8
MD5 b3d887946e9c4cfd1361d5713a73e735
BLAKE2b-256 c4eaf0cbe31b72246fa7870e3ce14f66c262a9103e58eaadb9f792a4a454a367

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page