✨ 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 📖

[](https://doi.org/10.1016/j.softx.2024.101758)

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 a full deployment of DeepTSF using Docker.

Set up locally using Docker

To set up locally using docker first go to DeepTSF's root directory (inside deeptsf_backend) 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!

Dagster UI for advanced users

For users that require advanced pipeline parameterization and functionalities such as hyperparameter tuning, a dagster based pipeline is provided. By modifying the config of deeptsfdagsterjob, the user can set all parameters described in the extensive documentation of DeepTSF. An example config file is given below:

resources: config: config: a: 0.3 analyze_with_shap: false convert_to_local_tz: true country: PT cut_date_test: "20210101" cut_date_val: "20200101" darts_model: LightGBM database_name: rdn_load_data device: gpu eval_method: ts_ID eval_series: eval_series evaluate_all_ts: true experiment_name: dagster_test forecast_horizon: 24 format: long from_database: false future_covs_csv: None future_covs_uri: None grid_search: false hyperparams_entrypoint: lags: [-1, -2, -14] ignore_previous_runs: true imputation_method: linear loss_function: mape m_mase: 1 max_thr: -1 min_non_nan_interval: 24 multiple: false n_trials: 100 num_samples: 1 num_workers: 4 opt_test: false order: 1 parent_run_name: dagster_test past_covs_csv: None past_covs_uri: None pv_ensemble: false resampling_agg_method: averaging resolution: 1h retrain: false rmv_outliers: true scale: true scale_covs: true series_csv: dataset-storage/Italy.csv series_uri: None shap_data_size: 100 shap_input_length: -1 std_dev: 4.5 stride: -1 test_end_date: None time_covs: false ts_used_id: None wncutoff: 0.000694 ycutoff: 3 ydcutoff: 30 year_range: None

For a more complete guide check the extensive documentation.

This application can also be deployed in a kubernetes enviroment.

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.

For the extensive DeepTSF documentation please navigate to our Wiki.

📺 DeepTSF — Video Demonstration

Also, a video demonstration of DeepTSF is avaialble on Youtube.

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.