Weighted Score-Oriented Losses for Temporally Localized Event Prediction

2026-06-22

AI Summary

This work addresses the mismatch between conventional loss functions and event-level evaluation metrics—such as F1 and Critical Success Index (CSI)—in temporal event prediction, particularly acute in anomaly or changepoint detection. To bridge this gap, the authors propose a temporally localized, weighted Score-Oriented Loss (wSOL), which for the first time incorporates temporal locality into the score-oriented learning framework. By employing differentiable, time-decaying weights, wSOL reduces penalties for near-miss false alarms and alleviates the cost of missed detections when early warnings are permissible. The method directly optimizes multiple event-level metrics and demonstrates significant improvements over cross-entropy and unweighted SOL across three benchmark datasets, especially in scenarios where labels do not explicitly encode temporal utility.

Abstract

Operational event-detection systems are rarely assessed by pointwise accuracy alone. In anomaly detection, changepoint detection, and warning systems, the utility of an alarm depends on its temporal position relative to an event. This produces a score-loss mismatch. Neural networks are commonly trained with classical loss functions, such as cross-entropy, whereas deployment decisions are obtained by thresholding network predictions, merging alarms through post-processing rules, and evaluating them with event-based metrics defined by detection windows and false-alarm costs. This paper studies a temporally localized specialization of weighted score-oriented loss (wSOL) for event prediction. Starting from score-oriented losses based on expected confusion matrices and from the weighted SOL framework of Marchetti et al., we consider temporal weights that discount near-event false positives and reduce false-negative penalties when an event is preceded by an admissible alarm. The resulting objective is differentiable with respect to the network predictions, and therefore can be optimized by back-propagation. It can be instantiated with balanced accuracy, true skill statistic, F1, critical success index, and related confusion-matrix scores. We evaluate the proposed approach by comparing cross-entropy, unweighted score-oriented loss, and wSOL on three benchmark datasets for time-series event prediction and detection. The results show that wSOL can improve performance when the evaluation utility is localized in time and is not already encoded by the pointwise labels.

Problem

• temporally localized event prediction
• score-loss mismatch
• event-based evaluation
• time-series anomaly detection
• weighted loss

Innovation

• weighted score-oriented loss
• temporal localization
• event prediction
• differentiable loss
• confusion matrix metrics