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End-to-end time series analysis and forecasting of monthly sunspot numbers using classical machine learning and deep learning models, including linear regression, tree-based methods, and advanced PyTorch architectures.
Reproducible pipeline that fetches and aligns NAO, ENSO, and sunspot time series into monthly/yearly panels for climate-driver correlation and predictive modeling.
Python toolkit correlating a GitHub user's commit history with solar and geomagnetic activity — sunspot numbers (SILSO), F10.7 flux, Dst and ap indices from OMNI2/NOAA SWPC. Computes lagged Pearson/Spearman correlations, Lomb-Scargle periodograms, and bootstrap CIs with heatmaps and multi-user comparison.
Fast, GPU-accelerated solar lucky-imaging stacker and sharpener for Windows, macOS and Linux. Published mirror of an internally developed project. Bug reports and feature requests welcome as issues; pull requests are not being accepted at the moment.
Analysis of ultraviolet emission from solar active regions using SDO/AIA 1600 Å and SDO/HMI observations, with active regions classified by their Hale magnetic configuration.
This project predicts sunspot activity using an LSTM model for time series data. Built with TensorFlow and Keras, it uses Huber loss for outlier handling and MAE for performance evaluation. The dataset, sourced from Kaggle or SIDC, spans over 270 years of monthly sunspot data.
Project aims at modeling the size distribution of sunspots greater than 60 millionths of a solar hemisphere (MSH) using a truncated log-normal distribution.