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 designed and trained to predict the evolution of a stochastic dynamical system%2C using wildfire prediction as a case study. We show that traditional evaluation methods based on threshold based classification metrics and error-based scoring rules assess a DNN's ability to replicate the observed ground truth (GT)%2C but do not measure the fidelity of the DNN's learning of the underlying stochastic process. To address this gap%2C we propose a new system property: Statistic-GT%2C representing the GT of the stochastic process%2C and an evaluation metric that exclusively assesses fidelity to Statistic-GT. Utilizing a synthetic dataset%2C we introduce a stochastic framework to characterize this property and establish criteria for a metric to be a valid measure of the proposed property. We formally show that Expected Calibration Error (ECE) tests the necessary condition for fidelity to Statistic-GT. We perform empirical experiments%2C differentiating ECE's behavior from conventional metrics and demonstrate that ECE exclusively measures fidelity to the stochastic process. Extending our analysis to real-world wildfire data%2C we highlight the limitations of traditional evaluation methods and discuss the utility of evaluating fidelity to the stochastic process alongside existing metrics.?quality=80&w=800)
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