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：本文提出DeepTSF，一个旨在通过工作流自动化和无代码建模创新时间序列预测的综合机器学习运维（MLOps）框架。DeepTSF 自动化了机器学习生命周期中的关键环节，使其成为从事基于机器学习（ML）和深度学习（DL）预测工作的数据科学家和 MLOps 工程师的理想工具。DeepTSF 为用户提供强大且易于使用的解决方案，同时其设计能够无缝集成到现有数据分析工作流中，从而提升生产力和兼容性。该框架提供适用于数据科学家及其他高层级利益相关者的前端用户界面（UI），通过富有洞察力的可视化和评估指标实现全面理解。DeepTSF 还通过身份管理和访问授权机制优先保障安全性。DeepTSF 在 I-NERGY 项目实际用例中的应用已证明了其在基于深度学习的负荷预测方面的有效性，展示了其在电力与能源系统领域的显著增值价值。