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https://github.com/wassname/DeepTime.git
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wassname
120 lines
4.7 KiB
Markdown
120 lines
4.7 KiB
Markdown
Fork note:
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Interested by the results on the Exchange data I'm digging deeper, plotting, extending, and replicating the exchange reults. See `scratch.ipynb`
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----
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# DeepTime: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting
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<p align="center">
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<img src=".\pics\deeptime.png" width = "700" alt="" align=center />
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<br><br>
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<b>Figure 1.</b> Overall approach of DeepTime.
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</p>
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Official PyTorch code repository for the [DeepTime paper](https://arxiv.org/abs/2207.06046).
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* DeepTime is a deep time-index based model trained via a meta-learning formulation, yielding a strong method for
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non-stationary time-series forecasting.
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* Experiments on real world datases in the long sequence time-series forecasting setting demonstrates that DeepTime
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achieves competitive results with state-of-the-art methods and is highly efficient.
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## Requirements
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Dependencies for this project can be installed by:
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```bash
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pip install -r requirements.txt
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```
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## Quick Start
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### Data
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To get started, you will need to download the datasets as described in our paper:
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* Pre-processed datasets can be downloaded from the following
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links, [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/e1ccfff39ad541908bae/)
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or [Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy?usp=sharing), as obtained
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from [Autoformer's](https://github.com/thuml/Autoformer) GitHub repository.
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* Place the downloaded datasets into the `storage/datasets/` folder, e.g. `storage/datasets/ETT-small/ETTm2.csv`.
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### Reproducing Experiment Results
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We provide some scripts to quickly reproduce the results reported in our paper. There are two options, to run the full
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hyperparameter search, or to directly run the experiments with hyperparameters provided in the configuration files.
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__Option A__: Run the full hyperparameter search.
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1. Run the following command to generate the experiments: `make build-all path=experiments/configs/hp_search`.
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2. Run the following script to perform training and evaluation: `./run_hp_search.sh` (you may need to
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run `chmod u+x run_hp_search.sh` first).
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__Option B__: Directly run the experiments with hyperparameters provided in the configuration files.
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1. Run the following command to generate the experiments: `make build-all path=experiments/configs/ETTm2`.
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2. Run the following script to perform training and evaluation: `./run.sh` (you may need to run `chmod u+x run.sh`
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first).
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Finally, results can be viewed on tensorboard by running `tensorboard --logdir storage/experiments/`, or in
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the `storage/experiments/experiment_name/metrics.npy` file.
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## Main Results
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We conduct extensive experiments on both synthetic and real world datasets, showing that DeepTime has extremely
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competitive performance, achieving state-of-the-art results on 20 out of 24 settings for the multivariate forecasting
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benchmark based on MSE.
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<p align="center">
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<img src=".\pics\results.png" width = "700" alt="" align=center />
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<br><br>
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</p>
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## Detailed Usage
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Further details of the code repository can be found here. The codebase is structured to generate experiments from
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a `.gin` configuration file based on the `build.variables_dict` argument.
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1. First, build the experiment from a config file. We provide 2 ways to build an experiment.
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1. Build a single config file:
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```
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make build config=experiments/configs/folder_name/file_name.gin
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```
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2. Build a group of config files:
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```bash
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make build-all path=experiments/configs/folder_name
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```
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2. Next, run the experiment using the following command
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```bash
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python -m experiments.forecast --config_path=storage/experiments/experiment_name/config.gin run
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```
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Alternatively, the first step generates a command file found in `storage/experiments/experiment_name/command`, which
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you can use by the following command,
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```bash
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make run command=storage/experiments/experiment_name/command
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```
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3. Finally, you can observe the results on tensorboard
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```bash
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tensorboard --logdir storage/experiments/
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```
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or view the `storage/experiments/deeptime/experiment_name/metrics.npy` file.
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## Acknowledgements
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The implementation of DeepTime relies on resources from the following codebases and repositories, we thank the original
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authors for open-sourcing their work.
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* https://github.com/ElementAI/N-BEATS
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* https://github.com/zhouhaoyi/Informer2020
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* https://github.com/thuml/Autoformer
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## Citation
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Please consider citing if you find this code useful to your research.
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<pre>@article{woo2022deeptime,
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title={DeepTime: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting},
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author={Gerald Woo and Chenghao Liu and Doyen Sahoo and Akshat Kumar and Steven C. H. Hoi},
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year={2022},
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url={https://arxiv.org/abs/2207.06046},
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}</pre>
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