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In analyses of rare-events, regardless of the domain of application,class-imbalance issue is intrinsic. Although the challenges are known to dataexperts, their explicit impact on the analytic and the decisions made based onthe findings are often overlooked. This is in particular prevalent ininterdisciplinary research where the theoretical aspects are sometimesovershadowed by the challenges of the application. To show-case theseundesirable impacts, we conduct a series of experiments on a recently createdbenchmark data, named Space Weather ANalytics for Solar Flares (SWAN-SF). Thisis a multivariate time series dataset of magnetic parameters of active regions.As a remedy for the imbalance issue, we study the impact of data manipulation(undersampling and oversampling) and model manipulation (using class weights).Furthermore, we bring to focus the auto-correlation of time series that isinherited from the use of sliding window for monitoring flares' history.Temporal coherence, as we call this phenomenon, invalidates the randomnessassumption, thus impacting all sampling practices including differentcross-validation techniques. We illustrate how failing to notice this conceptcould give an artificial boost in the forecast performance and result inmisleading findings. Throughout this study we utilized Support Vector Machineas a classifier, and True Skill Statistics as a verification metric forcomparison of experiments. We conclude our work by specifying the correctpractice in each case, and we hope that this study could benefit researchers inother domains where time series of rare events are of interest.

Challenges with Extreme Class-Imbalance and Temporal Coherence: A Study on Solar Flare Data