✨ DeepTSF is designed to enable codeless machine learning operations for time series forecasting ✨

🙌 Refer to https://github.com/epu-ntua/DeepTSF/wiki/DeepTSF-documentation for the documentation 📖

Installation

To set up DeepTSF on your local system, you need clone the main branch of this repository:

git clone https://github.com/epu-ntua/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).

Set up locally using Docker (Recommended)

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).

Bare metal installation

This installation is only recommended for advanced users that require advanced pipeline parameterization and functionalities such as hyperparameter tuning.

Set up mlflow tracking server

To run DeepTSF on your system you first have to install the mlflow tracking and minio server.

git clone https://github.com/epu-ntua/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.

Set up the DeepTSF backend (CLI functionality) locally using conda.

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.

References

[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.