# Weighted Score-Oriented Losses for Temporally Localized Event Prediction

arXiv: https://arxiv.org/abs/2606.23145 · HTML: https://mlsanity.com/paper/2606.23145/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 17 of 17)

- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Local Evaluation of Time Series Anomaly Detection Algorithms](https://mlsanity.com/paper/2206.13167/) (2022)
- [Precision and Recall for Time Series](https://mlsanity.com/paper/1803.03639/) (2018)
- [Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series](https://mlsanity.com/paper/2010.05073/) (2020)
- [Scalable Learning of Non-Decomposable Objectives](https://mlsanity.com/paper/1608.04802/) (2016)
- [Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series](https://mlsanity.com/paper/2303.01272/) (2023)
- [Online and Stochastic Gradient Methods for Non-decomposable Loss Functions](https://mlsanity.com/paper/1410.6776/) (2014)
- [TiSAT: Time Series Anomaly Transformer](https://mlsanity.com/paper/2203.05167/) (2022)
- [Surrogate regret bounds for generalized classification performance metrics](https://mlsanity.com/paper/1504.07272/) (2015)
- [Temporal Label Smoothing for Early Event Prediction](https://mlsanity.com/paper/2208.13764/) (2022)
- [Score-oriented loss (SOL) functions](https://mlsanity.com/paper/2103.15522/) (2021)
- [Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification](https://mlsanity.com/paper/1905.12511/) (2019)
- [Prediction of severe thunderstorm events with ensemble deep learning and radar data](https://mlsanity.com/paper/2109.09791/) (2021)
- [Bad and good errors: value-weighted skill scores in deep ensemble learning](https://mlsanity.com/paper/2103.02881/) (2021)
- [Dynamic Survival Analysis for Early Event Prediction](https://mlsanity.com/paper/2403.12818/) (2024)
- [A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics](https://mlsanity.com/paper/2305.13472/) (2023)
- [Onset and offset weighted loss function for sound event detection](https://mlsanity.com/paper/2403.13254/) (2024)

## Abstract (arXiv)

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.