  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.
