DeepTSF: Codeless machine learning operations for time series forecasting
Sotiris Pelekis,
Theodosios Pountridis,
Georgios Kormpakis,
George Lampropoulos,
Evangelos Karakolis,
Spiros Mouzakitis,
Dimitris Askounis
Affiliations
Sotiris Pelekis
Corresponding author.; Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Irron Polytechneiou 9, Zografou, 15772, Attica, Greece
Theodosios Pountridis
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Irron Polytechneiou 9, Zografou, 15772, Attica, Greece
Georgios Kormpakis
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Irron Polytechneiou 9, Zografou, 15772, Attica, Greece
George Lampropoulos
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Irron Polytechneiou 9, Zografou, 15772, Attica, Greece
Evangelos Karakolis
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Irron Polytechneiou 9, Zografou, 15772, Attica, Greece
Spiros Mouzakitis
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Irron Polytechneiou 9, Zografou, 15772, Attica, Greece
Dimitris Askounis
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Irron Polytechneiou 9, Zografou, 15772, Attica, Greece
This paper presents DeepTSF, a comprehensive machine learning operations (MLOps) framework aiming to innovate time series forecasting through workflow automation and codeless modeling. DeepTSF automates key aspects of the machine learning (ML) lifecycle, making it an ideal tool for data scientists and MLops engineers engaged in ML and deep learning (DL)-based forecasting. DeepTSF empowers users with a robust and user-friendly solution, while it is designed to seamlessly integrate with existing data analysis workflows, providing enhanced productivity and compatibility. The framework offers a front-end user interface (UI) suitable for data scientists, as well as other higher-level stakeholders, enabling comprehensive understanding through insightful visualizations and evaluation metrics. DeepTSF also prioritizes security through identity management and access authorization mechanisms. The application of DeepTSF in real-life use cases of the I-NERGY project has already proven DeepTSF’s efficacy in DL-based load forecasting, showcasing its significant added value in the electrical power and energy systems domain.
