# All-Clear Flare Prediction Using Interval-based Time Series Classifiers

arXiv: https://arxiv.org/abs/2105.01202 · HTML: https://mlsanity.com/paper/2105.01202/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 3 of 3)

- [A Time Series Forest for Classification and Feature Extraction](https://mlsanity.com/paper/1302.2277/) (2013)
- [A hybrid supervised/unsupervised machine learning approach to solar flare prediction](https://mlsanity.com/paper/1706.07103/) (2017)
- [Challenges with Extreme Class-Imbalance and Temporal Coherence: A Study on Solar Flare Data](https://mlsanity.com/paper/1911.09061/) (2019)

## Built on by (most cited first, 13 of 13)

- [Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting](https://mlsanity.com/paper/2209.07406/) (2022)
- [Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods](https://mlsanity.com/paper/2307.15878/) (2023)
- [Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting](https://mlsanity.com/paper/2308.02682/) (2023)
- [Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks](https://mlsanity.com/paper/2309.04558/) (2023)
- [Exploring Deep Learning for Full-disk Solar Flare Prediction with Empirical Insights from Guided Grad-CAM Explanations](https://mlsanity.com/paper/2308.15712/) (2023)
- [Embedding Ordinality to Binary Loss Function for Improving Solar Flare Forecasting](https://mlsanity.com/paper/2408.11768/) (2024)
- [Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions](https://mlsanity.com/paper/2309.14483/) (2023)
- [Active Region-based Flare Forecasting with Sliding Window Multivariate Time Series Forest Classifiers](https://mlsanity.com/paper/2402.03474/) (2024)
- [Towards Hybrid Embedded Feature Selection and Classification Approach with Slim-TSF](https://mlsanity.com/paper/2409.04542/) (2024)
- [VSFormer: Value and Shape-Aware Transformer with Prior-Enhanced Self-Attention for Multivariate Time Series Classification](https://mlsanity.com/paper/2412.16515/) (2024)
- [Uncertainty-Aware Solar Flare Regression](https://mlsanity.com/paper/2603.06712/) (2026)
- [Ordinal Encoding as a Regularizer in Binary Loss for Solar Flare Prediction](https://mlsanity.com/paper/2510.04063/) (2025)
- [Enhancing Explainability in Solar Energetic Particle Event Prediction: A Global Feature Mapping Approach](https://mlsanity.com/paper/2511.09475/) (2025)

## Abstract (arXiv)

An all-clear flare prediction is a type of solar flare forecasting that puts more emphasis on predicting non-flaring instances (often relatively small flares and flare quiet regions) with high precision while still maintaining valuable predictive results. While many flare prediction studies do not address this problem directly, all-clear predictions can be useful in operational context. However, in all-clear predictions, finding the right balance between avoiding false negatives (misses) and reducing the false positives (false alarms) is often challenging. Our study focuses on training and testing a set of interval-based time series classifiers named Time Series Forest (TSF). These classifiers will be used towards building an all-clear flare prediction system by utilizing multivariate time series data. Throughout this paper, we demonstrate our data collection, predictive model building and evaluation processes, and compare our time series classification models with baselines using our benchmark datasets. Our results show that time series classifiers provide better forecasting results in terms of skill scores, precision and recall metrics, and they can be further improved for more precise all-clear forecasts by tuning model hyperparameters.