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 ML lifecycle, making it an ideal tool for data scientists and MLops engineers engaged in machine learning (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.

翻译：本文介绍了DeepTSF，一个旨在通过工作流自动化和无代码建模革新时间序列预测的综合性机器学习运维（MLOps）框架。DeepTSF自动执行机器学习生命周期的关键环节，使其成为从事基于机器学习和深度学习预测的数据科学家及MLOps工程师的理想工具。该框架为用户提供强大且易用的解决方案，同时设计为可无缝集成现有数据分析工作流，提升生产力和兼容性。框架提供适合数据科学家及其他高层利益相关者的前端用户界面，通过直观的可视化与评估指标实现全面理解。DeepTSF还通过身份管理与访问授权机制优先保障安全性。I-NERGY项目实际应用案例已证明DeepTSF在基于深度学习的负荷预测中的有效性，彰显其在电力与能源系统领域的显著增值价值。