Skip to content

A code implementation of new papers in the time series forecasting field.

Notifications You must be signed in to change notification settings

hughxx/tsf-new-paper-taste

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

tsf-new-paper-taste License

A code implementation of new papers in the time series forecasting field.

Installation | Usage

One can just clone or download the project, then run the run.py.

The requirements that needed is very common, if necessary, one can install it by self.

Implemented Model

PatchMixer: https://arxiv.org/abs/2310.00655

SegRNN: https://arxiv.org/abs/2308.11200

iTransformer: https://arxiv.org/abs/2310.06625

TSMixer: https://arxiv.org/abs/2303.06053

Result

One can view result at below link, and add comment.

https://docs.google.com/spreadsheets/d/1_8WsqhCjRtVgLnGvE5VuGxUOPYzVuE9gog32o3I2LQA/edit?usp=sharing

Others

1. The model implementation strives to be as consistent as possible with the paper, but there is no guarantee of complete fidelity.

2. So far, some performance has not reached the level described in the paper, Welcome discuss and collaborate to improve it!

About

A code implementation of new papers in the time series forecasting field.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published