Sudden Event Prediction Accuracy

Updated 26 December 2025

SEPA is a metric assessing predictive models’ ability to identify rare, abrupt events across domains such as healthcare, traffic engineering, and space weather.
It integrates standard accuracy measures and threshold-free estimators to address challenges like class imbalance, sparse data, and censoring.
Real-world applications of SEPA enhance decision-making by isolating model responsiveness to critical, infrequent events that traditional metrics might overlook.

Sudden Event Prediction Accuracy (SEPA) quantifies the proficiency of predictive models in identifying abrupt, infrequent, and high-impact events across diverse domains including healthcare (sudden death), traffic engineering (abrupt congestion or recovery), and space weather (solar energetic particle events). SEPA encompasses event-focused accuracy statistics, event-centric detection algorithms, and threshold-free risk-score summaries. It is typically deployed in scenarios featuring class imbalance, sparse data, and potentially time-to-event or right-censored data structures.

1. Formal Definitions and Domain-Specific Instantiations

The term Sudden Event Prediction Accuracy (SEPA) is operationalized differently according to context, but it consistently focuses on the correct identification of predefined sudden events within a population or time series.

Binary/Time-Window Classification (e.g., sudden death, SEP events)

For binary prediction scenarios with a prespecified prediction horizon (e.g., 5 years for sudden death, 14 hours for solar events), SEPA is defined as the standard accuracy: $\mathrm{SEPA} = \mathrm{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}$ where TP, TN, FP, and FN denote counts of true positives, true negatives, false positives, and false negatives with respect to the event of interest (Jones et al., 2023, Kasapis et al., 4 Mar 2024).

Streaming, Event-Centric Scenarios (e.g., traffic state transitions)

In the context of time series forecasting where sudden events are defined operationally (e.g., traffic slowdowns or recoveries), SEPA measures the fraction of detected events that are correctly predicted within a user-defined tolerance: $\mathrm{SEPA} = \frac{\sum_{i=1}^N \sum_{t \in E_i} \mathbb{1}\{|\hat X_{i,t} - X_{i,t}| \le \delta_{\mathrm{tol}}\}}{\sum_{i=1}^N |E_i|}$ Here, $E_i$ are event times for node $i$ , $\delta_{\mathrm{tol}}$ is the error tolerance, and $\hat X_{i,t}$ is the model prediction at event time $t$ (Kralj et al., 19 Dec 2025).

Censored Time-to-Event Data

For risk scores in survival analysis, a threshold-free SEPA measure corresponds to the area under the time-dependent precision–recall curve (“average positive predictive value” [AP]): $\mathrm{SEPA}_{t_0} = \mathbb{E}_{Z \mid T < t_0}[\mathrm{PPV}_{t_0}(Z)]$ where

$\mathrm{PPV}_{t_0}(z) = \Pr\{T < t_0 \mid Z \ge z\},\quad \mathrm{TPF}_{t_0}(z) = \Pr\{Z \ge z \mid T < t_0\}$

This approach is robust to censoring and removes arbitrary threshold selection (Yuan et al., 2016).

2. Calculation Algorithms and Practical Implementation

Event-Centric Streaming Computation

For streaming/online environments, SEPA requires detection of sudden events in real-time, subject to parameters:

Event-detection window $H$
Change threshold $\delta_{\mathrm{change}}$
Prediction error margin $\delta_{\mathrm{tol}}$
Cooldown period $\tau_c$ to avoid repeated counting

Pseudocode involves:

Iterating over nodes and time;
Detecting events via change criteria over a sliding window;
Checking forecast error at event times;
Aggregating correct predictions.

All event detection and accuracy calculation steps—down to hyperparameter values—are explicitly specified in the source research (Kralj et al., 19 Dec 2025).

Threshold-Free Estimation Under Censoring

For right-censored time-to-event data, the inverse-probability-of-censoring weighted estimator is employed: $\widehat{\mathrm{SEPA}}_{t_0} = \sum_{j=1}^n \frac{w_j(t_0) \mathbb{1}\{X_j < t_0\}}{\sum_k w_k(t_0)\mathbb{1}\{X_k < t_0\}}\ \widehat{\mathrm{PPV}}_{t_0}(Z_j)$ where weights $w_j$ are derived from Kaplan-Meier estimates of censoring survival (Yuan et al., 2016). Empirical performance is validated via simulation and bootstrapping.

Supervised Classification Settings

For tabular or signal data, standard binary accuracy, $F_1$ , ROC AUC, TSS, and other confusion-matrix-based metrics are reported with bootstrapped confidence intervals (Jones et al., 2023, Kasapis et al., 4 Mar 2024).

3. Comparative Analysis: SEPA Versus Standard Metrics

SEPA highlights model responses to the rare, impactful events that may be masked by overall error metrics:

In traffic prediction, conventional MAE/RMSE metrics are dominated by stable periods, while SEPA isolates model responsiveness to abrupt state changes. Models with good MAE can have SEPA near zero if they ignore sudden events (Kralj et al., 19 Dec 2025).
In risk prediction, SEPA (as average PPV) can reveal clinically meaningful improvements in precision that are not detectable by ROC AUCs, especially in low-prevalence settings (Yuan et al., 2016).
For solar event forecasting, SEPA under operational protocols matches overall accuracy, while skill scores such as TSS and HSS provide further discrimination (Kasapis et al., 4 Mar 2024).

Metric	Captures Sudden Events?	Threshold-Free?	Robust to Censoring?
SEPA	Yes	Sometimes	Some forms (AP only)
AUC	No	Yes	Yes
MAE/RMSE	No	N/A	No
F₁-score	Yes (with tuning)	No	No

Models with high SEPA but modest global error reveal specialization in predicting rare events.

4. Empirical Benchmarking and Sensitivity

Multiple studies document SEPA’s empirical range, sensitivity, and domain dependence:

In sudden death prediction (EHR cohort, 5-year horizon), best model SEPA was 0.75 (95% CI 0.74–0.76), with event incidence ≈2% (Jones et al., 2023).
In real-time traffic (PeMS-BAY, 60-min horizon), SEPA varied from ≈29% (no neighbor connectivity), 33% (adaptive), to 34% (full), with rare events making up 0.3% of time steps (Kralj et al., 19 Dec 2025).
For forecasting solar energetic particle events (14-hour horizon), best-case SEPA was 0.70 ± 0.09 (balanced), 0.56 ± 0.04 (operational, imbalanced) (Kasapis et al., 4 Mar 2024).
For censored survival endpoints, SEPA (as AP) ranged from 4–11% in low-prevalence cohorts, detecting significant increases attributable to richer features, even where AUC improved marginally (Yuan et al., 2016).

Sensitivity to connectivity, input pruning, class imbalance, event rarity, and window size is documented. For instance, traffic SEPA remained robust with up to 70% graph pruning; in survival data, the AP estimator remains stable at event rates as low as 1% for moderate $n$ .

5. Interpretability, Model Consensus, and Domain Insights

SEPA not only measures raw event-detection accuracy but can be leveraged to enhance interpretability and clinical/operational trust:

Model-Consensus Analysis: Rank Biased Overlap (RBO) is used to assess convergence of top features across models. High RBO correlates with stable SEPA and suggests reproducibility of signal, especially in sparse-event scenarios (Jones et al., 2023).
Feature Attribution: In linear and kernel machines for SEP event prediction, feature coefficients rank drivers of SEPA; e.g., unsigned flux near polarity inversion lines (R_VALUE) is a dominant predictor (Kasapis et al., 4 Mar 2024).
Operational Adaptivity: SEPA can feed directly into control algorithms (e.g., adaptive graph pruning), dynamically trading communication/resource usage for event-detection performance (Kralj et al., 19 Dec 2025).

6. Limitations and Appropriate Use

SEPA’s event-centered focus entails several caveats:

For “smooth” performance or stability outside sudden events, traditional metrics (MAE, overall ROC AUC) remain necessary; SEPA must be interpreted as a complement (Kralj et al., 19 Dec 2025).
For extremely low event rates and small samples, SEPA estimates may be noisy, though windowed aggregation and bootstrapping mitigate variance.
Threshold-free AP-based SEPA depends on event base rate, requiring normalization or adjustment for cross-population comparisons (Yuan et al., 2016).

SEPA’s primary strength resides in contexts where safety, operational response, or rare-event identification is the critical evaluative standard.

7. Domain-Specific Applications and Future Directions

SEPA has demonstrated relevance and technical maturity across several research frontiers:

Healthcare: Risk prediction for sudden death in primary prevention cohorts, model adjudication for rare adverse events (Jones et al., 2023, Yuan et al., 2016).
Intelligent Transportation: Online monitoring and decentralized traffic control via spatial graphs, optimizing communication while maximizing event-responsiveness (Kralj et al., 19 Dec 2025).
Space Weather: Predictive modeling of SEP events threatening space systems, benchmarking progress towards physics-informed classification (Kasapis et al., 4 Mar 2024).

Extension to financial collapse, critical infrastructure failure, and network cyber-intrusion is plausible, provided event definitions and time windows are prospectively specified.

A plausible implication is that ongoing methodological advances—especially in interpretability, class imbalance adjustment, and censoring-robust estimation—will further broaden SEPA’s role as the central metric for quantifying actionable, rare-event forecasting across scientific and engineering disciplines.