[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118785-en":3,"doc-seo-118785-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118785,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","DEEPTSF - CODELESS MACHINE LEARNING OPERATIONS FOR TIME SERIES FORECASTING","DeepTSF introduces a comprehensive MLOps framework that advances time series forecasting through workflow automation and codeless modeling. The framework streamlines major stages of the machine learning lifecycle, supporting both data scientists and MLOps engineers working with ML and deep learning forecasting pipelines. A front-end UI and visualization-driven evaluation metrics enhance interpretability for multiple stakeholders while maintaining integration with existing data analysis workflows. DeepTSF also emphasizes security via identity management and access authorization, and its use in the I-NERGY project validates effectiveness in deep learning load forecasting for electrical power and energy systems.","arXiv :2308 .00709v 1 [ cs .LG] 28 Jul 2023  \nDEEPTSF: CODELESS MACHINE LEARNING OPERATIONS FOR  \nTIME SERIES FORECASTING  \nSotiris Pelekis, Evangelos Karakolis, Theodosios Pountridis, George Kormpakis, George Lampropoulos, Spiros  \nMouzakits, and Dimitris Askounis  \nDecision Support Systems Laboratory  \nSchool of Electrical and Computer Engineering  \nInstitute of Communications and Computer Systems  \nNational Technical University of Athens  \nGreece  \nAugust 3, 2023  \nABSTRACT  \nThis paper presents DeepTSF, a comprehensive machine learning operations (MLOps) framework aiming to innovate time series forecasting through workflow automation and codeless modeling.  \nDeepTSF 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.  \nDeepTSF 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.  \nThe framework offers a front-end user interface (UI) suitable for data scientists, as well as other higher-level stakeholders, enabling comprehensive understanding through insightful visualizationsand 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.  \nKeywords automation, codeless, deep learning, machine learning operations, time series forecasting, software  \n1 Motivation and significance  \nHistorically, time-series modeling has been a prominent area of interest in academic research, with diverse applications in fields such as climate modeling [42], biological sciences [60], medicine [63], and commercial decision-making domains like retail [59], finance [56], and energy [49, 48] . Traditional approaches in this field have primarily focused on parametric statistical models, utilizing domain expertise-driven techniques such as autoregressive models [13], exponential smoothing [23], and other methods that heavily relied on decomposing time series [8] . However, the advent of modern ML methods has introduced data-driven approaches for capturing temporal dynamics [37] .  \nAmong these methods, deep learning (DL) has gained significant traction, inspired by its remarkable achievements in areas like image classification [31], natural language processing [66], and reinforcement learning [34] . Deep neural networks, with their customized architectural assumptions or inductive biases [10], can effectively learn intricate data representations, eliminating the need for manual feature engineering and model design. The availability of open-source backpropagation frameworks [46, 1] has further simplified network training, allowing for flexible customization of network components and loss functions. This convergence of data availability, increased computing power, and the rise of deep learning has transformed time-series forecasting, paving the way for the next generation of models. These models leverage ML techniques to effectively capture complex temporal dependencies, facilitating improved forecasting accuracy and flexibility in various applications.  \nA PREPRINT-AUGUST 3, 2023  \nNomenclature  \nANN Artificial neural network  \nAPI Application programming interface CLI Command line interface  \nCNN Convolutional neural network DL Deep learning  \nGPU Graphics processing unit LSTM Long short-term memory LW Lookback window  \nMAPE Mean absolute percentage error MLOps Machine learning operations MLP Multi-layer perceptron  \nMLR Multiple linear regression  \nN-BEATS Neural basis expansion analysis for time series forecasting RMSE Root mean squared error  \nRNN Recurrent neural networksNaive Seasonal naive  \nSTL","cbCaibTlkXUVBQXv","https://ap.wps.com/l/cbCaibTlkXUVBQXv","pdf",3299063,1,28,"English","en",105,"# Motivation and significance\n## Historical foundations of time-series modeling\n## Rise of deep learning for temporal dynamics","[{\"question\":\"What problem does DeepTSF target in time series forecasting?\",\"answer\":\"DeepTSF targets the need to innovate time series forecasting by automating the ML workflow and enabling codeless modeling within an MLOps framework.\"},{\"question\":\"What capabilities does DeepTSF provide to support the ML lifecycle?\",\"answer\":\"DeepTSF automates key steps of the ML lifecycle, including continuous training, evaluation, validation, and deployment, replacing manual processes in conventional pipelines.\"},{\"question\":\"How does DeepTSF address security for users and stakeholders?\",\"answer\":\"DeepTSF prioritizes security through identity management and access authorization mechanisms integrated into the framework.\"}]","DEEPTSF - 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