first commit

README.md

@@ -0,0 +1,87 @@
+# DeepTIMe: Deep Time-Index Meta-Learning for Non-stationary Forecasting
+
+<p align="center">
+<img src=".\pics\deeptime.png" width = "700" alt="" align=center />
+<br><br>
+<b>Figure 1.</b> Overall approach of DeepTIMe.
+</p>
+
+Official PyTorch code repository for the DeepTIMe paper.
+
+## Requirements
+
+Dependencies for this project can be installed by:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Quick Start
+
+### Data
+
+To get started, you will need to download the datasets as described in our paper:
+
+* Pre-processed datasets can be downloaded from the following
+  links, [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/e1ccfff39ad541908bae/)
+  or [Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy?usp=sharing), as obtained
+  from [Autoformer's](https://github.com/thuml/Autoformer) GitHub repository.
+* Place the downloaded datasets into the `storage/datasets/` folder, e.g. `storage/datasets/ETT-small/ETTm2.csv`.
+
+### Reproducing Experiment Results
+
+We provide some scripts to quickly reproduce the results reported in our paper. There are two options, to run the full
+hyperparameter search, or to directly run the experiments with hyperparameters provided in the configuration files.
+
+__Option A__: Run the full hyperparameter search.
+
+1. Run the following command to generate the experiments: `make build-all path=experiments/configs/hp_search`.
+2. Run the following script to perform training and evaluation: `./run_hp_search.sh` (you may need to
+   run `chmod u+x run_hp_search.sh` first).
+
+__Option B__: Directly run the experiments with hyperparameters provided in the configuration files.
+
+1. Run the following command to generate the experiments: `make build-all path=experiments/configs`.
+2. Run the following script to perform training and evaluation: `./run.sh` (you may need to run `chmod u+x run.sh`
+   first).
+
+Finally, results can be viewed on tensorboard by running `tensorboard --logdir storage/experiments/`, or in
+the `storage/experiments/experiment_name/metrics.npy` file.
+
+## Detailed Usage
+
+Further details of the code repository can be found here. The codebase is structured to generate experiments from
+a `.gin` configuration file based on the `build.variables_dict` argument.
+
+1. First, build the experiment from a config file. We provide 2 ways to build an experiment.
+    1. Build a single config file:
+       ```
+       make build config=experiments/configs/folder_name/file_name.gin
+       ```
+    2. Build a group of config files:
+       ```bash
+       make build-all path=experiments/configs/folder_name
+       ```
+2. Next, run the experiment using the following command
+    ```bash 
+    python -m experiments.forecast --config_path=storage/experiments/experiment_name/config.gin run
+   ```
+   Alternatively, the first step generates a command file found in `storage/experiments/experiment_name/command`, which
+   you can use by the following command,
+   ```bash
+   make run command=storage/experiments/experiment_name/command
+   ```
+3. Finally, you can observe the results on tensorboard
+   ```bash
+   tensorboard --logdir storage/experiments/
+   ``` 
+   or view the `storage/experiments/deeptime/experiment_name/metrics.npy` file.
+
+## Acknowledgements
+
+The implementation of DeepTIMe relies on resources from the following codebases and repositories, we thank the original
+authors for open-sourcing their work.
+
+* https://github.com/ElementAI/N-BEATS
+* https://github.com/zhouhaoyi/Informer2020
+* https://github.com/thuml/Autoformer