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Found 11 papers, 3 papers with code
Multiscale Dubuc: A New Similarity Measure for Time Series
In this study, however, we present a different similarity measure that fuses the notion of Dubuc's variation from fractal analysis with the Intersection-over-Union (IoU) measure which is widely used in object recognition (also known as the Jaccard Index).
Measuring Class-Imbalance Sensitivity of Deterministic Performance Evaluation Metrics
In this paper, we introduce an intuitive evaluation framework that quantifies metrics' sensitivity to the class imbalance.
Improving Solar Flare Prediction by Time Series Outlier Detection
While a number of machine-learning methods have been proposed to improve flare prediction, none of them, to the best of our knowledge, have investigated the impact of outliers on the reliability and those models' performance.
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods
We incorporated 24 Feature Subset Selection (FSS) algorithms, including multivariate and univariate, supervised and unsupervised, wrappers and filters.
Multiscale IoU: A Metric for Evaluation of Salient Object Detection with Fine Structures
General-purpose object-detection algorithms often dismiss the fine structure of detected objects.
Towards Synthetic Multivariate Time Series Generation for Flare Forecasting
We utilize a flare forecasting benchmark dataset, named SWAN-SF, and design two verification methods to both quantitatively and qualitatively evaluate the similarity between the generated minority and the ground-truth samples.
How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events
We present a case study of solar flare forecasting by means of metadata feature time series, by treating it as a prominent class-imbalance and temporally coherent problem.
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.
Challenges with Extreme Class-Imbalance and Temporal Coherence: A Study on Solar Flare Data
This is in particular prevalent in interdisciplinary research where the theoretical aspects are sometimes overshadowed by the challenges of the application.
Toward Filament Segmentation Using Deep Neural Networks
Despite the known challenges in the identification and characterization of filaments by the existing module, which in turn are inherited into any other module that intends to learn from such outputs, Mask R-CNN shows promising results.
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission
We provide a large image parameter dataset extracted from the Solar Dynamics Observatory (SDO) mission's AIA instrument, for the period of January 2011 through the current date, with the cadence of six minutes, for nine wavelength channels.
