Abstract
LTSF Linear is an open-source Data Science project. [AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?". This repo is the official Pytorch implementation of LTSF-Linear: "Are Transformers Effective for Time Series Forecasting?". It is built using Python, Deep Learning, PyTorch. Key capabilities include: Add a benchmark for long-term time series forecasting; Support both Univariate and Multivariate long-term time series forecasting; Support visualization of weights. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
This repo is the official Pytorch implementation of LTSF-Linear: "Are Transformers Effective for Time Series Forecasting?".
Besides LTSF-Linear, we provide five significant forecasting Transformers to re-implement the results in the paper.
The implementation of Autoformer, Informer, and Transformer is from https://github.com/thuml/Autoformer
2. Objective
[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"
This project demonstrates how Python, Deep Learning, PyTorch can be applied to a real-world Data Science problem.
3. Key Features / Modules
- Add a benchmark for long-term time series forecasting.
- Support both Univariate and Multivariate long-term time series forecasting.
- Support visualization of weights.
- Support scripts on different look-back window size.
- Transformer (NeuIPS 2017)
- Informer (AAAI 2021 Best paper)
- Autoformer (NeuIPS 2021)
- Pyraformer (ICLR 2022 Oral)
- FEDformer (ICML 2022)
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/cure-lab/LTSF-Linear.git
cd LTSF-LinearFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Turn the analysis into an interactive dashboard
- Automate data refresh with a scheduled job
- Add a predictive model on top of the analysis
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by cure-lab and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.
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