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

TL;DR

This paper develops an all-clear solar flare prediction system using interval-based time series classifiers, emphasizing high precision in predicting non-flaring instances to reduce false alarms.

Contribution

It introduces the use of Time Series Forest classifiers for all-clear flare prediction and demonstrates their effectiveness over baseline models in this context.

Findings

Time series classifiers outperform baselines in skill scores.

Hyperparameter tuning improves prediction precision.

The approach effectively balances false negatives and positives.

Abstract

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.