FlaPLeT: A full-stack web platform for end-to-end time series data processing and machine learning in solar flare prediction

Abstract

Solar flare prediction is a central challenge in space weather forecasting, with direct implications for satellite operations, aviation safety, and power grid reliability. Machine learning has achieved state-of-the-art performance for this task, particularly when applied to photospheric magnetic field parameters. FlaPLeT is an open-source, full-stack web platform that supports end-to-end machine learning workflows for multivariate time-series–based solar flare prediction without requiring any coding expertise. Built with React, Django, Celery, and PostgreSQL, the system integrates dataset preprocessing, data augmentation, functional network (graph) construction, and machine learning model training into modular asynchronous tasks that generate downloadable datasets, trained models, and structured JSON reports. The platform is deployed on a dedicated Windows server using NGINX, Waitress, Redis, TLS encryption, and reCAPTCHA to ensure secure and scalable operation. FlaPLeT lowers the barrier for heliophysicists to apply machine learning to photospheric magnetic field data and to systematically evaluate how preprocessing strategies and hyperparameter choices affect flare-prediction accuracy. Its cloud-based deployment removes local hardware constraints and makes the platform accessible to researchers worldwide through a standard web browser.