thuml/Nonstationary_Transformers
Code release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415
This project helps data scientists and machine learning engineers improve the accuracy of their time series predictions. It takes raw, real-world time series data, like electricity consumption or traffic flow, and produces more reliable forecasts than standard Transformer models. The primary users are researchers and practitioners working on advanced time series forecasting problems.
558 stars. No commits in the last 6 months.
Use this if you are a data scientist or researcher struggling with poor accuracy in long-term time series forecasting using existing Transformer-based models.
Not ideal if you need a simple, off-the-shelf solution for basic time series forecasting without deep engagement in model architecture.
Stars
558
Forks
102
Language
Python
License
MIT
Category
Last pushed
Aug 19, 2024
Commits (30d)
0
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