Papers

• Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition (2025)
  Zeinab Hassani et al.
  4.98
• Beyond prewhitening: detection of gravity modes and their period spacings in slowly pulsating B stars using the multitaper F-test (2025)
  Aarya A. Patil et al.
  1.56
• StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars (2025)
  Weijian Li et al.
  1.44
• Beyond Point Matching: Evaluating Multiscale Dubuc Distance for Time Series Similarity (2025)
  Azim Ahmadzadeh et al.
  1.44
• ARCANE -- Early Detection of Interplanetary Coronal Mass Ejections (2025)
  H. T. R\"udisser et al.
  1.17
• Method for estimating cycle lengths from multidimensional time series: Test cases and application to a massive "in silico" dataset (2016)
  Nigul Olspert et al.
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• Fast and scalable Gaussian process modeling with applications to astronomical time series (2017)
  Daniel Foreman-Mackey et al.
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• Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods (2021)
  Atharv Yeoleka et al.
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• Solar Active Regions Detection Via 2D Circular Kernel Time Series Transformation, Entropy and Machine Learning Approach (2023)
  Irewola Aaron Oludehinwa et al.
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• Time Series of Magnetic Field Parameters of Merged MDI and HMI Space-Weather Active Region Patches as Potential Tool for Solar Flare Forecasting (2024)
  Paul A. Kosovich et al.
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• Active Region-based Flare Forecasting with Sliding Window Multivariate Time Series Forest Classifiers (2024)
  Anli Ji and Berkay Aydin
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• Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches (2024)
  Sabrina Guastavino et al.
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• Analysis and Predictive Modeling of Solar Coronal Holes Using Computer Vision and ARIMA-LSTM Networks (2024)
  Juyoung Yun et al.
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• Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive Learning (2024)
  MohammadReza EskandariNasab et al.
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