DeepTSF: Codeless machine learning operations for time series forecasting

by   Sotiris Pelekis, et al.

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.


page 4

page 5

page 8

page 12

page 13

page 14


Deep Learning for Energy Time-Series Analysis and Forecasting

Energy time-series analysis describes the process of analyzing past ener...

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019

Financial time series forecasting is, without a doubt, the top choice of...

Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices

Machine Learning (ML) and Deep Learning (DL) methods are increasingly re...

Darts: User-Friendly Modern Machine Learning for Time Series

We present Darts, a Python machine learning library for time series, wit...

Systematic review of deep learning and machine learning for building energy

The building energy (BE) management has an essential role in urban susta...

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures

Background: We previously presented GraphVar as a user-friendly MATLAB t...

Please sign up or login with your details

Forgot password? Click here to reset