This is the repository for DeepTSF timeseries forecasting tool. The whitepaper for this project can be found in [1]. For the extensive DeepTSF documentation please navigate to our Wiki.
To set up DeepTSF on your local system, you need clone the main branch of this repository:
git clone https://github.com/I-NERGY/DeepTSF.git
Alternatively you can use the dedicated Github release instead of cloning the main branch.
After that you need to navigate to the root directory of DeepTSF:
cd /path/to/repo/of/DeepTSF
Το enable the communication of the client with the logging servers (MLflow, Minio, Postgres), a .env file is needed. An example (.env.example) is provided, with default environment variables.
After that, you can set up DeepTSF either using conda (CLI for data scientists) or Docker (full deployment).
To set up locally using docker first go to DeepTSF's root directory and rename .env.example to .env. Then run the following command in DeepTSF's root directory:
docker-compose up
DeepTSF is up and running. Navigate to http://localhost:3000 and start your experiments!
- Optional step for advanced users:
In a new terminal window, you can copy the timeseries file you desire to run into the container, and then run the following to gain access to the container's file system:
docker cp <path_to_file> DeepTSF-backend:/app
docker exec -it DeepTSF-backend bash
Now you are running bash in the main DeepTSF container! Choose the directory which best corresponds to your
problem, and switch to that:
- uc2 for general problems. The app will execute a national load forecasting
use case if from_database is set to true. So preferrably set from_database=False unless you create your own database connection.
- uc6 and uc7 are related to other use cases and are still under development.
So run the following to set up your working environment:
cd uc2
conda activate DeepTSF_env
export MLFLOW_TRACKING_URI=https://localhost:5000
export GIT_PYTHON_REFRESH=quiet
Then, you can execute any experiment you want. An example working command (also demonstrated in the whitepaper [1]), is shown below:
mlflow run --experiment-name example --entry-point exp_pipeline . -P series_csv=user_datasets/Italy.csv -P convert_to_local_tz=false -P day_first=false -P from_database=false -P multiple=false -P imputation_method=peppanen -P resolution=1h -P rmv_outliers=true -P country=IT -P year_range=2015-2022 -P cut_date_val=20200101 -P cut_date_test=20210101 -P test_end_date=20211231 -P scale=true -P darts_model=NBEATS -P hyperparams_entrypoint=NBEATS_example -P loss_function=mape -P opt_test=true -P grid_search=false -P n_trials=100 -P device=gpu -P ignore_previous_runs=t -P forecast_horizon=24 -P m_mase=24 -P analyze_with_shap=False --env-manager=local
Don't forget to change series_csv argument to match the file's location in the container (if you followed the previous instructions it must be located in the parent directory).
This installation is only recommended for advanced users that require advanced pipeline parameterization and functionalities such as hyperparameter tuning.
To run DeepTSF on your system you first have to install the mlflow tracking and minio server.
git clone https://github.com/I-NERGY/mlflow-tracking-server.git
cd mlflow-server
After that, you need to get the server to run
docker-compose up
The MLflow server and client may run on different computers. In this case, remember to change the addresses on the .env file.
You can use conda.yaml to reproduce the conda environment manually. Simply execute the following command which creates a new conda enviroment called DeepTSF_env:
cd /path/to/repo/of/DeepTSF
conda env create -f conda.yaml
Then activate the new environment:
conda activate DeepTSF_env
Alternatively to those 2 commands, you can reproduce the conda environment automatically,
by running any 'mlflow run' command without the option --env-manager=local.
This option however is not encouraged for every day use as it rebuilds the conda environment from scratch every time.
Then, set the MLFLOW_TRACKING_URI to the uri of the mlflow tracking server (by default http://localhost:5000). Please do not omit this step as this environment variable will not get inherited from the .env file.
export MLFLOW_TRACKING_URI=https://localhost:5000
For the extensive DeepTSF documentation please navigate to our Wiki.
[1] S. Pelekis et al., “DeepTSF: Codeless machine learning operations for time series forecasting,” SoftwareX, vol. 27, p. 101758, Sep. 2024, doi: 10.1016/J.SOFTX.2024.101758.