https://github.com/weifantt/DEPTS

https://github.com/weifantt/DEPTS

deep-learning timeseries-forecasting

Last synced: 13 days ago
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• Host: GitHub
• URL: https://github.com/weifantt/DEPTS
• Owner: weifantt
• Created: 2022-02-21T13:01:42.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-03-31T09:11:05.000Z (over 2 years ago)
• Last Synced: 2024-08-01T16:33:16.110Z (3 months ago)
• Topics: deep-learning, timeseries-forecasting
• Language: Python
• Homepage:
• Size: 20.5 KB
• Stars: 40
• Watchers: 2
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# DEPTS

Source code for the paper,

["DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting"](https://openreview.net/forum?id=AJAR-JgNw__),

in ICLR22 Spotlight.

## Overview

DEPTS is a customized deep neural network architecture for periodic time series forecasting, which aims to solve the following two challenges:

- To capture diversified periodic compositions

- To model complicated periodic dependencies

## Dataset

You can download the five benchmarks from [Google Drive](https://drive.google.com/file/d/1GYt2chsZLbmJkNG3lb-ytCrI3nuZKDxm/view?usp=sharing). All the datasets are well pre-processed. More details of datasets can be found in the [paper](https://openreview.net/forum?id=AJAR-JgNw__). After downloading the zip file, please unzip it to the root dir of DEPTS for experiments.

## Usage

### Setup

Please use `Python 3(.6)`  as well as the following packages:

```text

torch >= 1.6.0

dataclasses

dtaidistance

pandas

numpy

tqdm

```

### Reproduce

To reproduce the results, you can see more details in `command.sh` and directly run:

```text

sh command.sh

```

Note that all the results reported in the paper are ensembled results of 30 models in order to get a robust evaluation and compare with [N-BEATS](https://arxiv.org/abs/1905.10437). You can also try to run the single model for evaluation if you find it challenging to run all the models.

### Evaluation

To get the evaluation results, run

```text

python evaluation.py

```

## Citation

If you find our work interesting, you can cite the paper as

```text

@inproceedings{

fan2022depts,

title={{DEPTS}: Deep Expansion Learning for Periodic Time Series Forecasting},

author={Wei Fan and Shun Zheng and Xiaohan Yi and Wei Cao and Yanjie Fu and Jiang Bian and Tie-Yan Liu},

booktitle={International Conference on Learning Representations},

year={2022},

url={https://openreview.net/forum?id=AJAR-JgNw__}

}

```