anm-frets-pyraformer's Introduction

FreTS and Pyraformer

This is an open-source library of the implementation of:

FreTS - Frequency-domain MLPs are More Effective Learners in Time Series Forecasting [NeurIPS 2023]
Pyraformer - Pyraformer: Low-complexity Pyramidal Attention for Long-range Time Series Modeling and Forecasting [ICLR 2022]

The repo is for the course project of Advanced Network Management (ANM) of Tsinghua University. The code base is mainly modified from Time Series Library (TSlib).

Usage

Install Python 3.8. We recommand using Anaconda to manage your environment. For example, you can create an environment with the following command.
```
conda create -n ANM python=3.8
```
After creating the environment, execute the following command to activate it.
```
conda activate ANM
```
Execute the following command to install requiements.
```
pip install -r requirements.txt
```
The requirements are verified with Ubuntu 22.04 and Nvidia RTX 3090 with CUDA 12.2. If you encounter an error like this RuntimeError: CUDA error: no kernel image is available for execution on the device in the folloing steps, you may need to reinstall a compatible version of your environment, see Pytorch.
Prepare Data. Place the downloaded datasets in the folder./dataset. The datasets used in this project are provided by TA.
Train and evaluate model. We provide the experiment scripts for all benchmarks under the folder ./scripts/. You can reproduce the experiment results as the following examples:
```
bash ./scripts/FreTS_ETT.sh
```
or run all experiments with
```
bash ./run.sh
```
If you only want to run some tests after training, execute
```
bash ./scripts/test-only/Tests.sh	# only contains four configurations to reproduce the figures in the report
```
Checkpoints are stored in ./checkpoints/*/ in pth format. Test results are stored in ./results/*/ in npy format. Visualization of predictions are stored in ./test_results/*/ in pdf format.

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