Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Active Region-based Flare Forecasting with Sliding Window Multivariate Time Series Forest Classifiers

Anli Ji, Berkay Aydin

TL;DR

The paper addresses solar flare forecasting by integrating temporal evolution in active regions through a sliding-window, multivariate time-series framework. It introduces interval-based feature extraction from sliding windows and trains a random forest on these interval features, coupled with a feature-ranking mechanism to identify the most informative sub-intervals. The approach yields strong forecast skill (TSS around 0.82–0.85) and provides interpretable insights into which time intervals and features drive predictions, including regional indicators like TOTUSJH and SAVNCPP. This work enhances operational flare forecasting by offering transparent, interval-level explanations and robust performance across multiple window configurations.

Abstract

Over the past few decades, many applications of physics-based simulations and data-driven techniques (including machine learning and deep learning) have emerged to analyze and predict solar flares. These approaches are pivotal in understanding the dynamics of solar flares, primarily aiming to forecast these events and minimize potential risks they may pose to Earth. Although current methods have made significant progress, there are still limitations to these data-driven approaches. One prominent drawback is the lack of consideration for the temporal evolution characteristics in the active regions from which these flares originate. This oversight hinders the ability of these methods to grasp the relationships between high-dimensional active region features, thereby limiting their usability in operations. This study centers on the development of interpretable classifiers for multivariate time series and the demonstration of a novel feature ranking method with sliding window-based sub-interval ranking. The primary contribution of our work is to bridge the gap between complex, less understandable black-box models used for high-dimensional data and the exploration of relevant sub-intervals from multivariate time series, specifically in the context of solar flare forecasting. Our findings demonstrate that our sliding-window time series forest classifier performs effectively in solar flare prediction (with a True Skill Statistic of over 85\%) while also pinpointing the most crucial features and sub-intervals for a given learning task.

Active Region-based Flare Forecasting with Sliding Window Multivariate Time Series Forest Classifiers

TL;DR

The paper addresses solar flare forecasting by integrating temporal evolution in active regions through a sliding-window, multivariate time-series framework. It introduces interval-based feature extraction from sliding windows and trains a random forest on these interval features, coupled with a feature-ranking mechanism to identify the most informative sub-intervals. The approach yields strong forecast skill (TSS around 0.82–0.85) and provides interpretable insights into which time intervals and features drive predictions, including regional indicators like TOTUSJH and SAVNCPP. This work enhances operational flare forecasting by offering transparent, interval-level explanations and robust performance across multiple window configurations.

Abstract

Over the past few decades, many applications of physics-based simulations and data-driven techniques (including machine learning and deep learning) have emerged to analyze and predict solar flares. These approaches are pivotal in understanding the dynamics of solar flares, primarily aiming to forecast these events and minimize potential risks they may pose to Earth. Although current methods have made significant progress, there are still limitations to these data-driven approaches. One prominent drawback is the lack of consideration for the temporal evolution characteristics in the active regions from which these flares originate. This oversight hinders the ability of these methods to grasp the relationships between high-dimensional active region features, thereby limiting their usability in operations. This study centers on the development of interpretable classifiers for multivariate time series and the demonstration of a novel feature ranking method with sliding window-based sub-interval ranking. The primary contribution of our work is to bridge the gap between complex, less understandable black-box models used for high-dimensional data and the exploration of relevant sub-intervals from multivariate time series, specifically in the context of solar flare forecasting. Our findings demonstrate that our sliding-window time series forest classifier performs effectively in solar flare prediction (with a True Skill Statistic of over 85\%) while also pinpointing the most crucial features and sub-intervals for a given learning task.
Paper Structure (12 sections, 5 equations, 4 figures, 1 table)

This paper contains 12 sections, 5 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Problem Formulation
  5. Interval Features
  6. Time Series Forest and Feature Ranking
  7. Experimental Evaluations
  8. Data Collection
  9. Experimental Settings
  10. Comparison between different window sizes
  11. Class weights tuning
  12. Conclusions and Future Work

Figures (4)

  • Figure 1: An X9.3-class flare occurred in 2017-09-06 captured by Solar Dynamics Observatory (a) and its accompanying coronal mass ejection captured by Large Angle and Spectrometric Coronagraph (LASCO) instrument (b)
  • Figure 2: The overview of the methodology presented in this paper. We first generate subsequences (intervals) with a sliding window. Then, we create vectorized features from these intervals where these features can be used as input for the sliding window time series forest (a random forest built on multivariate time series features) and features are ranked with aggregated relevance scores.
  • Figure 3: Results of our feature ranking experiments with only mean ($f_{mean}$ of slices used in the feature set and secondary transformation applied. Results come from an aggregation of five different imbalance weight settings and bars show how many times a feature has appeared in the top-5 ranking list. Orange bars show the features obtained after secondary transformations while blue ones show the local features. Note that WS represents the window size while SS represents the step size of candidate interval settings.
  • Figure 4: Results of our feature ranking experiments with only standard deviation ($f_{std}$ of slices used in the feature set and secondary transformation applied (similar to Fig. \ref{['fig:top5_mean']}). Results come from an aggregation of five weight settings and bars show how many times a feature has appeared in the top-5 ranking list. Orange bars show the features obtained after secondary transformations and blue ones show the local features.

Theorems & Definitions (4)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4