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Found 10 papers, 5 papers with code
EXCON: Extreme Instance-based Contrastive Representation Learning of Severely Imbalanced Multivariate Time Series for Solar Flare Prediction
In heliophysics research, predicting solar flares is crucial due to their potential to impact both space-based systems and Earth's infrastructure substantially.
M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps
Machine learning (ML) models for multivariate time series classification have made significant strides and achieved impressive success in a wide range of applications and tasks.
SeriesGAN: Time Series Generation via Adversarial and Autoregressive Learning
Current Generative Adversarial Network (GAN)-based approaches for time series generation face challenges such as suboptimal convergence, information loss in embedding spaces, and instability.
Forest Proximities for Time Series
We also use the forest proximities alongside Local Outlier Factors to investigate the connection between misclassified points and outliers, comparing with nearest neighbor classifiers which use time series distance measures.
Contrastive Representation Learning for Predicting Solar Flares from Extremely Imbalanced Multivariate Time Series Data
In this paper, we introduce CONTREX, a novel contrastive representation learning approach for multivariate time series data, addressing challenges of temporal dependencies and extreme class imbalance.
Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive Learning
Accurate solar flare prediction is crucial due to the significant risks that intense solar flares pose to astronauts, space equipment, and satellite communication systems.
ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series Generation
This advanced framework integrates the benefits of an Autoencoder-generated embedding space with the adversarial training dynamics of GANs.
Motif-guided Time Series Counterfactual Explanations
We propose Motif-Guided Counterfactual Explanation (MG-CF), a novel model that generates intuitive post-hoc counterfactual explanations that make full use of important motifs to provide interpretive information in decision-making processes.
Shapelet-Based Counterfactual Explanations for Multivariate Time Series
In this work, we take advantage of the inherent interpretability of shapelets to develop a model agnostic multivariate time series (MTS) counterfactual explanation algorithm.
Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists.
