EVEREST: A Transformer for Probabilistic Rare-Event Anomaly Detection with Evidential and Tail-Aware Uncertainty
Authors: Antanas Žilinskas
Robert Noel Shorten
Jakub Marecek
Venue: ICLR 2026 Poster (2026)
Keywords: Transformer models
Uncertainty quantification
Evidential deep learning
Extreme value theory
Imbalanced classification
Primary area: learning on time series and dynamical systems
TL;DR: EVEREST is a transformer architecture for rare-event time-series forecasting that combines evidential and tail-aware uncertainty to deliver calibrated, interpretable, and state-of-the-art predictions across scientific anomaly detection tasks.

Abstract: Forecasting rare events in multivariate time-series data is a central challenge in machine learning, complicated by severe class imbalance, long-range dependencies, and distributional uncertainty. We introduce EVEREST, a transformer-based architecture for probabilistic rare-event forecasting that delivers calibrated predictions and tail-aware risk estimation, with auxiliary interpretability through attention-based signal attribution. EVEREST integrates four key components: (i) a learnable attention bottleneck for soft aggregation of temporal dynamics; (ii) an evidential head for estimating aleatoric and epistemic uncertainty via a Normal–Inverse–Gamma distribution; (iii) an extreme-value head that models tail risk using a Generalized Pareto Distribution; and (iv) a lightweight precursor head for early-event detection. These modules are jointly optimised with a composite loss combining focal loss, evidential negative log-likelihood, and a tail-sensitive EVT penalty, and act only at training time; deployment uses a single classification head with no inference overhead. We evaluate EVEREST on a real-world benchmark spanning a decade of space-weather data and demonstrate state-of-the-art performance, including True Skill Statistic (TSS) scores of 0.973, 0.970, and 0.966 at 24, 48, and 72-hour horizons for C-class flares. The model is compact (≈0.81M parameters), efficient to train on commodity hardware, and applicable to other high-stakes domains such as industrial monitoring, weather, and satellite diagnostics. Limitations include reliance on fixed-length inputs and exclusion of image-based modalities, motivating future extensions to streaming and multimodal forecasting.

EVEREST:
AN EVIDENTIAL, TAIR-AWARE TRANSFORMER FOR RARE-EVENT TIME-SERIES FORECASTING
Antanas Zilinskas
Robert N. Shorten
Imperial College London
Imperial College London
antanas. zilinskas researchhgmaaaaae com
r shorten@imperiiie ac uk
Jakub Mareček
Czech Technical University in Prague, Faculty of Electrical Engineering jakub www.u...aaceeeeee.o evut.
ABSTRACT
Forecasting rare events in multivariate time-series data is challenging due to severe class imbalance, long-range dependencies, and distributional uncertainty. We in- troduce EVEREST, raasformer-basee architecture for probabilistic rare-event forecasting that delivers calibrated predictions and tail-aware risk estimation, with auxiliary interpretability via attention-basee signal attribution. EVEREST inte- grates four components; (i) a learnable attention bottleneck for soft aggregation of temporal dynamics; (ii) an evidential head for estimating aleatoric and epis- temic uncertainty via a Jormaa--nverse--e -ammma distribution; (iii) an extreme- value head that models tail risk using a Generalized Pareto Distribution; and (iv) lightweight precursor head for early-event detection. These modules are jointly optimized with a composite loss (focal loss, evidential NLL, and a tail-sensitive EVT penalty) and act only training time; deployment uses single classifi- cation head with no inference overhead (approximately 0.81M parameters). On decade of space-weather data, EVEREST achieves state-of-the-art True Skill Statistic (TSS) of 1.973/0.970/0..
at 24/48/72-hour horizons for C-class flares. The model compact, efficient train on commodity hardware, and applica- ble to high-stakes domains such as industrial monitoring, weather, and satellite diagnostics. Limitations include reliance on fixed-length inputs and exclusion of image-based modalities, motivating future extensions to streaming and multi- modal forecasting.
INTRODUCTION
Rare, high-impact events in multivariate time series pose a central challenge in machine learning, with direct implications for space weather, industrial monitoring, power systems, and satellite health. Models must contend with three factors simultaneously: severe class imbalance, long-range tempo- ral dependencies that dilute early precursors, and the need for calibrated probabilities and explicit tail-risk assessment in thresholded, operational decision makinng. Standard objectives under-weight extremes, and average losses (e.g.. cross-entropy) provide little guidance in settings where false negatives are disproportionately costly.
First, extreme rarity and long horizons make discriminative learning difficult: positive sequences are sparse, contexts are long, and recent long-horizon architectures from frequency-based decompositions (Zhou et al. 2022) to patching (Nie et al.) (023) and modern convolutions (Luo & Wang -mprove aggregation but do not directly address calibration under rarity. Prior forecasting models such as recurrence-bar based approaches (Liu et al. 2019). hy- brid -rannfaneforrer designs (Sun et al. 2022). and flare-specifi architectures (Abduallah al.) 2023) achieve strong discrimination but provide limited tools for calibrated uncertainty or tail be- haviour. Second, high-stakes applications require reliable probabilities: miscalibration degrades
These challenges are threefold.

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operational utiliiy,, especially for rare-event thresholds where uncertainty decomposition (aleatoric vs. eeisttmmicc matters for decision support (Sensoy et al. 2018 Amini et al. 2020 van Amersfoort et al. 2020). Third, catastrophic outcomes occupy the far tail, where standard losses provide lit-
tle gradient signal; modelling exceedances beyond high quantile is well studied in extreme-value
theory (EVT) (Coles) 2001 de Haan & Ferreira 2006), but rarely coupled with neural sequence models.
Here we show that these three challenges can be jointly addressed in single, compact trans- former. We introduce EVEREST, which integrates a learnable single-query attention bottleneck for long-range temporal aggregation with three training-only auxiliaries: an evidential Normal- Tvvese-GGamne (NIG) head that regularises logit calibration, an EVT exceedance head that shapes far-tail behaviour using a Generalised Pareto penalty, and lightweight precursor head that imposes anticipatory supervision. These components act solely during training: inference uses single clas- sification head, so runtime cost identical to a standard transformer of comparable size. Across nine SHARP-GOES folar-flare tasks (2010-2023), EVEREST achieves state-of-the-art TSS (c.g., 0.973/0 07/3//0970/..0. /0.96 for 2C at 24/48/72 h and 0. 0.907 2007/0.90 / 0.996/00000. /0.966 for 2M5) with strong calibra- tion (e.g., h ECE 0.016). The model also transfers without architectural changes to an industrial anomaly dataset (SKAB), reaching F1 = 98.16% and TSS- = 0.964.
The design is intentionally practical and general: single encoder and bottleneck aggregate tem- poral evidence; training-only auxiliaries regularise the shared representation; inference remains lightweight (0.81M parameters, auxiliary heads). We provide extensive ablations quantifying the marginal utility of each component and analyse reliability, tail sensitivity, and thresholded deci- sion performance. We further provide systematic sensitivity analyses over evidential and EVT loss weights and exceedance quantiles, showing that performance is robust across wide hyperparameter ranges-consine consistent with these terms acting as regularisers rather than fragile knobs.
The remainder of the paper situates the method among recent time-series transformers, calibration approaches, and EVT-based models; details the backbone, bottleneck, auxiliaries, and composite loss; and presents results, ablations, robustness studies, and limitations. We conclude by discussing broader applicability in scientific and industrial forecasting, where compact, calibrated, tail-aware models are increasingly required.
RELATED WORK
Our work draws on long history of work scattered across multiple research communities. Let us present these in turn:
Rare-event time series and imbalance. Forecasting rare events in multivariate time series re- quires handling both severe class imbalance and long temporal dependencies. Early approaches of- ten treated each window independently, combining hand-crafted features with a classifier, whereas modern deep models such as TCNs and Transformers exploit sequential structure more effectively. Cost-sensitive objectives like focal loss address imbalance without altering the data distribution, while aggressive oversampling can introduce temporal artefacts or leakage, motivating loss-based rather than batad rebalancing strategies for scientific and operational settings. Recent super- vised pipelines report strong solar-flare discrimination at 24-72h horizons, including CNN/RNN hybrids and flare-specific Transformers (Liu et al. 2019 Sun al., 2022 Abduallah et al. We report results on the same SHARP-GOES benchmark.
2023
Transformers for time series. Transformers have become highly competitive in time-series fore- casting, but naïve self-attention incurs O cost. Recent architectures address this by restructur- ing temporal information through patch/token re-organization with channel-firs first encoders (Nie et al.) 2023), frequency- or decompssition-base
long-horizon modules (Zhou et al. 2022), or inverted de- signs that summarize time before mixing channels (Liu et al. 2024). Pure-convolutional models can rival attention on long sequences at lower computational cost (Luo Wang 2024). Our single- query attention bottleneck provides lightweight, cessdconnit global aggregator in this design space, conceptually closer to attention pooling and global token mechanisms (Isse et al. 2018 Lec
et al. 2019) than to full self-attention over all time steps.
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Calibration and evidential learning. High-stakes forecasting requires not only discriminative curacy but also reliable probabilities. Beyond common metrics such as TSS or AUPRC, calibration metrics (ECE, Brier score) directly inform operational thresholding. Post-hoc strategies such as temperature scaling (Guo et al.) 2017) can improve marginal reliability but cannot recover input- conditional epistemic uncertainty. Deterministic OOD surrogates and deep ensembles (van Amers- foort et al. 20200) Lakshminarayanan et al.) 2017) offer stronger uncertainty estimates at higher computational cost, Evidential methods instead learn closed-form distributional parameters, e.g. Dirichlet for classification or Normal-Inverse Gamma (NIG) for regression, enabling uncertainty decomposition without Monte Carlo sampling (Sensoy et al.) 2018 Amiii al.) (020). Com- plementary developments in conformal prediction provide
error control in time series (Ding et al. 2023). We adopt an evidential NIG head directly on the logit to regularise calibration during training.
Tail risk and EVT in machine learning. Standard objectives under-weight catastrophic extremes because they allocate little gradient mass to high-quantile regions. The moekk-over-threshhod frame- work from extreme value theory (EVT) offers principled treatment of distribution tails using Gen- eralized Pareto exceedances (Coles 200T de Haan Ferreira 2006). Recent work has applied EVT to extreme-event prediction directly time-series signals (ozerawski et al. (022). Our ap- proach differs that the GPD fitted to logit exceedances, allowing EVT to act as a training- time tail-shaping regulariser rather than post-hoe residual-basee tail estimator.
Auxiliary/precursor supervision and multi-task learning. Auxiliary tasks can improve pri- mary task by regularising a shared backbone, even when auxiliary heads are removed at inference, demonstrated in classical and modern multi-task learning (Caruana 1997; (Standley al. 2020). Contrastive forecasting objectives (Makansi et al.) (202T) similarly inject early-event structure by contrasting imminent-event windows against quiescent ones. Although EVEREST does not employ contrastive learning, its lightweight precursor head plays a related role by encouraging anticipatory representations: integrating a contrastive variant is a natural direction for future work.
Industrial anomaly benchmarks. The SKAB dataset provides multivariate valve traces com- monly used in time-series anomaly detection (Filonov et al. 2020). Among strong baselines, TranAD reports leading performance across datasets (Tuli al. 2022). For comparability, we adopt the same windowing, labelling, and evaluation protocol used in published work.
Gap and positioning, Most prior work improves sequence encoders or enhances calibration, but rarely addresses calibration and tail sensitivity jointly in a compact architecture. EVEREST fills this gap by combining (i) single-query attention bottleneck for long-context aggregation, (ii) an evidential NIG head for closed-form logit calibration, and (iii) an EVT exceedance penalty to em- phasise extremes all optimised jointly while retaining single classification head at test time with no inference overhead.
3 METHOD
We consider binary rare-event forecasting on multivariate time series. Each example window X E RTXF, containing T' time steps and F features, with label y E {0. 1} indicating whether an event occurs within a fixed forecast horizon. The model outputs a logit / E IR and probability produce an alert. We report skill with the True Skill Statistic (TSS) and assess reliability with the Brier score and Expected Calibration Error (ECE).
á = (l) E [0, 1], which is compared decision threshold
For clarity. we summarise the main notation used in this section. Encoder layer outputs hidden states ( evidential head outputs Normal- Inverre--aammaa parameters (/4, u, a, 88) over the logit, and the EVT head predicts Generalised Pareto parameters (5, for exceedances above high quantile The composite loss is controlled by non-negative coefficients (A,, dos X Ap). full notation table is provided in Appendix K
which the attention bottleneck pools into single representation The
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3.1 ARCHITECTURE OVERVIEW
The network comprises four stages: (i) an input embedding with scaled positional encoding, (ii) a 6x Transformer encoder, (iii) single-query attention bottleneck that aggregates the sequence into sin- gle latent vector 2, and (iv) a shallow shared MLP (128-d) from which four parallel heads branch: a primary binary classification logit (used inference) and three training-only muxiliaries-evideee (NIG), EVT (GPD) exceedance, and a lightweight precursor head.
Unless explicitly stated otherwise, deployment uses only the classification head in a single forward pass. The evidential, EVT, and precursor heads act as training- time auxiliaries that regularise the shared representation and can be evaluated offline for diagnostics, are never required for test-time decisions.
In the embedding and transformer backbone, cf. (i) and (ii) above, raw inputs are projected to d-dimensional tokens and combined with sinusoidal positional codes scaled by learnable global factor a:
ho LN( bemb), where Wemb E R.XF and bemb E IRd are learned.
H Drop(ho + PE),
We apply L-6 encoder blocks with multi-head self-attention and position-wise feed-forward net- works:
0 LN(H Drop|MHHA(Er
H LN( Drop(FFN((E())))
for 1.....6.. 6. The reference setting ($4) uses embedding dimension d=1288 L=6 layers, H=4 attention heads, FFN width 256, and dropout 0=0.0
In the attention bottleneck, cf. (iii) above, one undertakes temporal focussing. Let H [hi,.... hrl E RdxT denote the final encoder states and w R learned scorer. We compute a single soft attention distribution over time and the pooled vector
a₁ = softmax ht),
2 Σ ai he, W. E Rd.
This single-query bottleneck adds only +d parameters and (TT) flops, yet concentrates capacity on weak, distributed precursors that global average pooling tends to dilute. In ablations ($5). re- placing the bottleneck with mean pooling substantially reduces skill (e.g.. ATSS +0.427 on the hardest M5-76 72 task).
Finally, the pooled representation feeds four parallel linear heads that share the backbone and MLP parameters, cf. (iv) above.
Classification head. The primary head produces a scalar logit
1 = Warz + berr
with probability á = ol) used for all thresholded decisions.
Evidential (NIG) head. The evidential head predicts parameters (μ, 0, a. 3) of Nor- mal-Inverse-Gamma distribution over the logit I, and minimises a closed-form evidential objec- tive, yielding analytic predictive mean and variance without Monte Carlo sampling. This acts as Bayesian surrogate that regularises logit-level uncertainty. ablations it primarily improves dis- crimination on the hardest tasks (e.g., ATSS +0.064 on M5-72h; $5) while maintaining low ECE.
EVT (GPD) head. The EVT head predicts Generalised Pareto parameters (ç, for logit ex- ceedances above a high batchwise quantile (90% by default). For logits (1:) a mini-batch, form exceedances {4 regulariser on (E, o to avoid degenerate tails. This shifts gradient mass towards the risky upper tail and improves rare-event sensitivity.
4 u) and maximise the GPD log-likelihood with small stability
Precursor (auxiliary) head. The precursor head reuses the same binary label and is trained via binary cross-entropy
auxiliary objective providing anticipatory supervision. It is not used at inference. In ablations, removing it degrades M5-72h TSS by -0.650 ($5). indicating that early supervision materially shapes the backbone.
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3.2 COMPOSITE LOSS AND TRAINING SCHEDULE
The training objective unifies four complementary criteria- disrriinadiiinninaiinn calibration, tail aware- ness, and anticipatory supervision- a single composite loss:
L= Xy Lrocal + de Levid + X Lev + Ap Spres-
Only the relative values of (Ay, de: Ae, Ap) matter, since any common scaling leaves the optimiser invariant; in practice we parameterise these as normalised weights (up to shared scale factor) and use (Aç, Aes X,, Ap)
(0.8, 0.1, 0.1, 0.05) the reference setting. The decreasing values are inspired by the Information Bottleneck principle (Tishby et al.) 20000: the encoder compresses inputs X into latent while maximising mutual information T(2; Y) with the event label. Each loss term targets distinct aspect of this balance: Liocal improves separation under extreme rarity, Levid regularises predictive entropy, Levt reallocates capacity toward tail exceedances, shaping the heavy tail, and Lere enriches I(Z;Y] Y) with anticipatory structure. Together, they yield encoder that balances predictive skill with uncertainty fidelity under extreme rarity, where the auxiliary heads provide calibrated and tail-sensitive regularisation:
Focal discrimination: The focal term Lrocal addresses class imbalance by re- re-weighting mis- classified examples according their difficulty. With focusing parameter y, it emphasises hard rare-event examples:
N Σ (1- y. log2: + y; log(1 p.)]
Liocal
We anneal 0 2 linearly over the first 50 epochs, initially allowing broad exploration and later sharpening emphasis on difficult rare-event instances.
Evidential calibration: The evidential term Levid learns NIG parameters over the logit, in- ducing predictive distribution with closed-form mean and variance. This encourages the model to represent epistemic and aleatoric uncertainty at the logit level, providing a cali- brated probability surface without sampling. In practice, ablations show small effects on ECE but consistent gains in TSS on the most imbalanced tasks ($5).
Tail emphasis via EVT: The EVT term Lest fits Generalised Pareto Distribution to logit exceedances above a high quantile u. For batch of logits (4), exceedances {z, = 4 u 4 > u} are modelled via
1/E
Pr(l > u L> u)
E2r
with (g. a) predicted by the EVT head. Maximising the GPD log log-likelihood reallocates gradient signal to rare. high-risk predictions, aligning optimisation with extreme- value the- ory and improving sensitivity in the far tail.
Precursor supervision: The precursor term Eree applies binary cross-entropy the precur- sor head using the same label y. It acts as anticipatory supervision, encouraging the latent Z to encode early discriminative cues rather than only near-term features. From the IB perspective, it enriches 1(2; Y) by regularising Z toward features predictive of both early and late outcomes.
these weights and the EVT quantiles show that performance is robust over a wide range of hyperparameters, consistent with the auxiliaries acting regularisers rather than fragile knobs. All four losses act only at training time; deployment uses the classification head ò = (I), with uncertainty and tail diagnostics from the evidential and EVT heads evaluated offline if desired.
As discussed in $ sensitivity analyses
Computational footprint. At the reference configuration, EVEREST has approximately 8. x 10Sh parameters and 1.66 x 10" FLOPs per window; the six-layer backbone accounts for more than 97% both, while the attention bottleneck adds only +d parameters, A full med-me budget and comparison Solar FlareNet (Abduallah al. 2023) provided in Appendix A
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4 EXPERIMENTAL SETUP
4.1 DATASETS AND SPLITS
Solar flares (SEAR)-GOES). We adopt the SHARP- GOES protocol and splits consistent with prior work (Abduallah et al. 2023): SHARP vector-magnetogram parameters aligned to GOES flare labels across Solar Cycle 24 25, with standard quality masks (QUALITY=0, CCD] & 70", observer radial-velocity filter) applied before windowing. We use the same nine SHARP parameters and the same window construction for 24/48/72h horizons. To prevent leakage, we use the iden- tical HARPNUM-stratifeed train//alidaatinn/ess split; the resulting per-horizon, per-class counts are consolidated in Appendix B (Table 55. All preprocessing (normalization, cadence handling, label alignment) follows that setup to ensure 1:1 comparability.
SKAB (industrial transfer). We evaluate cross-domain transfer on the Skoltech Anomaly Bench- mark (SKAB) (Filonov et al.; 2020) using fixed-length windows (stride two), stacked raw+diff chan- nels, chronological 70/15/15 splits, and standardization fitted on train only. We do not apply over- sampling task-specific loss reweighting. TranAD is the strongest published reference (Tuli al.) 2022). Full data-processing protocol, model configuration, and the complete results/comparisons are provided in Appendix C (Tables 6
4.2 METRICS AND EVALUATION PROTOCOL
Primary and secondary metries. Our primary discrimination metric is the True Skill Statistic (TSS),
TSS
TP
FP FF+TN
TP4FN
reported at the task-specific operating threshold 7 (below). We also report Precision/Recall/F1,, AUROC and AUC for ranking quality, and the Brier score for probabilistic accuracy. Reliability quantified via Expected Calibration Error (ECE) with equal-fiequency binning (15 bins).
Operating thresholds and cost sensitivity. Decision thresholds are selected by grid search over E (0.10, 0.10,,..1.......... o.., 0.90) using the balanced score (40% TSS, 20% F1, 15%% Precision, 15% Recall, 10% Specificity). For sensitivity to asymmetric costs, we complement this with a cost-loss sweep (c.g., 220:C1p=20:1) and report the minimum-cost threshold a alongside the balanced operating point.
4.3 TRAINING DETAILS AND HPO
All models are trained in PyTorch with automatic mixed precision (AMP), AdamW (81=0.9, 32=99. cosine-decayed learning rate, gradient-norm clipping (1.0), and the composite objective from $ with A= 0.1, 0.1, and focal annealed 0-2 over the first 50 epochs. Hyper-parameter optimization follows the three-stage protocol (Sobol scan Optuna refinement variance: em-
+ confirmation), limited to the six knobs that explained the bulk of validation- bedding width d, encoder depth L, dropout p, focal y, peak LR 7/max and batch size B. The search priors and the final chosen configuration are in Appendix D per-scenario optima are tabulated in Appendix D.4
Statistical protocol. For each threshold-horizon task we train five seeds and report means with 95% CIs via 104-draw bootstrap on the held-out test set, stratified by NOAA active-region identifier to preclude temporal leakage. Operating thresholds are selected by grid over E {0.10.... 0.90} (step 0.01) using a balanced score (40% TSS, 20% F1, 15% Precision, 15% Recall, 10% Specificity); unless stated, headline metrics use the task-specific r* from this procedure. The composite- loss weights and related hyperparameters are fixed across all tasks. We also filter obviously failed runs and report detectable effects (c.g. ATSS 0.02) alongside p-values from the bootstrap test.
4.4 FIGURES AND TABLES FOR REPRODUCIBILITY
To keep the setup self-contained within the page budget, we reuse the same artefacts and protocol as released implementation:

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SHARP feature list and motivations (Table 4): the nine input parameters with brief phys-
ical rationale.
Dataset distribution (Table 5 counts per horizon, class, and split under HARPNOM strat- ification.
CMD filtering diagram (Fig. 2) effect of the |CMD| S 70 mask on the usable sequence pool during solar data pre-processing.
RESULTS
5.1 HEADLINE PERFORMANCE
We compare against three published forecasters that span the main model families on SHARP- GOES: (1) LSTM recurrent predictor (Liu al. 2019). (2) 3D-CNN (Sun al. (022). and (3) SolarFlareNet (Abduallah et al. 2023). the strongest published baseline. Brief architectural sum- maries and reproducibility details are provided in Appendix L EVEREST shows large TSS gains across horizons, with especially strong improvements for rare M5 events. All nine tasks exceed the reported baseline TSS values (Table []. Table [2 reports bootstrapped metrics; EVEREST deliv- ers consistently high discrimination for common C-class events (TSS 2 0.966 all horizons) and strong performance for rarer and M5 classes. See $5.2for calibration diagnostics, and Appendix for full per-tast task results and operating thresholds.
5.2 CALIBRATION AND RELIABILITY
We report calibration with Brier score and Expected Calibration Error (ECE; 15 equal-frequency bins) alongside TSS. On the most imbalanced task (M5-72h) we obtain ECE 0.016 with near- diagonal reliability curve: similar trends hold for C-72h and M-72 h. Diagnostics use the same seeds, splits, and binning as the headline metrics in Table 12 in Appendix F
Full reliability diagrams are provided
5.3 DECISION ANALYSIS UNDER ASYMMETRIC COSTS
Operational use often values missed-event costs far above false alarms. For M5-72h h, a cost-loss sweep with Crv:Cpp=20:1 yields minimum-cost threshold of r* 0.240, distinct from the balanced-score = 0.460. Figure illustrates the trade-off; the corresponding confusion matrices
in Appendix G
By threshold class. >C: TSS remains within 0.973/0.977 77730/90///0966 0.996 (24/48/72 with precision
0.999/00.992 and minor horizon decay (ATSS= 0.007 from 24h to 72 2M: Despite stronger imbalance, TSS reaches 0.898 00////99900099/0 with recall 2 0.908; precision gains with hori- zon (0.728 > 0.834). > MS: For the rarest events, TSS 0.907 1000770..36/0.00000 00.96/00.666 with tight CIs and the best ECE (e.g., 0.016 at h2.
Comparison to prior work. Table summarizes TSS versus reported baselines. Our reported scores are higher than published baseline values (e.g., +0.251 TSS for 2C-48h and +0.237 for 2M5-72h), h). Significance testing is applied within our models.
This explicit operating-point choice addresses decision relevance under asymmetric costs without retraining, and the full confusion matrices are provided in Appendix G
5.4 ABLATIONS
leave-one-component-onr suite (five seeds each) quantifies the marginal utility of each module on the hardest task hM)-72h). Headline effect sizes are:
Attention bottleneck: +0.427 TSS over mean pooling.
EVT head: +0.285 TSS with major extreme-Bris Brier gains.
Evidential NIG head: +0.064 TSS with lower ECE.

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Loss Analysis:
Ratio FN:FP
vs Threshold
1.0
Spon 6633
Con-Drtimal
Optimat 0.483
2.
Farected CAR Sarree
044
32
3.5
0.2
Pritabillyy
0.4
05
0.5
Figure 1:
Cost-loss analysis for the M5-72h model under asymmetric costs (CEN:CFP = 20:1). The left panel shows the cost curve; the right panel highlights the minimum-cost threshold 7* 0.240 versus the balanced-score threshold - = 0.460.
Table TSS performance across flare thresholds and horizons. Bold indicates the best performance within each horizon. Reported values for EVEREST are mean (standard deviation) over 5 seeds.
Method
Horizon
2C
MM
2M5.0
Liu et al. (2019)
24h
0.612
0.792
0.881
Sun et al. (2022)
24h
0.756
0.826
Abduallah al. (2023)
24h
0.835
0.839
0.818
48h
0.719
0.728
0.736
72h
0.702
0.714
0.729
EVEREST
24h
0.973 (0.001)
0.898 (0.011)
0.907 (0.025)
48h
0.970 (0.001)
0.920 (0.007)
0.936 (0.021)
72h
0.966 (0.001)
0.906 (0.012)
0.966 (0.024)
Composite schedule: +00045 TSS from 7y annealing and stable joint training.
Removing the precursor auxiliary degrades performance by -0.650 TSS, showing that anticipatory supervision materially shapes the backbone even though is discarded inference. Mixed-precisioe (AMP) was also indispensable: FP32 runs diverged or underperformed. In addition, a 5x5 log-scale sweep over the evidential and EVT loss weights shows that both TSS and ECE remain stable across wide regions of the (Aevid, Aevi) grid, and an EVT quantile sweep over E {0.85, 0.90, 0.95) on the hardest task (M5-72h) yields TSS in [0.903, 0.932) and ECE [0.0119, 0.0164) across all runs, indicating insensitivity to the precise exceedance threshold. Full ver-variant metrics, significance tests, calibration effects, the X-sensitivity heatmaps, and EVT-quantile analysis are consolidated in Appendix H
5.5 INTERPRETABILITY
Saliency analysis highlights how EVEREST differentiates between prediction outcomes. True pos- itives show coordinated increases in USFLUX and MEANGAM in the final hours before the forecast horizon, consistent with flux emergence and seld-inclination steepening. True negatives and false positives exhibit flatter or noisier signatures. Confidence-stratifie TP cases show that gradients are strongest when predictive confidence is high. Full gradient visualisations are provided in Appendix[]
5.6 PROSPECTIVE CASE STUDY
We evaluate EVEREST on the unseen Sep 2017 X9.3 flare (NOAA AR 12673), the largest event of Solar Cycle 24. Data from 3-7 September 2017 were excluded from training and threshold calibration. The probability trace and lead-time statistics are provided in Appendix (Figure and and Table 18).
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5.7 CROSS-DOMAIN TRANSFER: SKAB
With the architecture unchanged, EVEREST achieves mean TSS 0.964 and Fl 98.16% SKAB (Filonov et al. 2020). We include SKAB because is multivariate, sure-event-orientee and widely used in anomaly detection; baseline results (e.g., TranAD) (ull et al. 2022). Full valve-level metrics and calibration diagnostics are in Appendix
5.8 EFFICIENCY SNAPSHOT
Training uses AMP and the composite schedule from $ yet compute-dense (16.6M FI.OPs/reference shape), with mean epoch times ~24 RTX A6000 and ~69 s on M2 Pro; full energy and carbon accounting appears in the supplement; results remain within typical "Green AI" norms for this model scale.
The model is compact (814k params)
Summary, Across nine tasks, EVEREST reports higher TSS than the baselines with strong cali- bration, clear module-level attributions for its gains, and actionable threshold analyses. The same backbone generalises to SKAB without architectural changes.
CONCLUSION
We presented EVEREST, compact, domain-agnostic Transformer and unified training recipe for rare-event time series that jointly targets discrimination, calibration, and tail-risk. From an infor- mation-bottleneck perspective (Tishby al.) (000), the model shapes latent representation Z that preserves maximal mutual information with the event label Y while discarding nuisance variability. Each auxiliary term enforces distinct view of this principle: focal loss drives separation under rarity, the evidential head regularises predictive entropy, the EVT penalty reallocates gradient mass to tail exceedances, and the precursor head biases compression toward anticipatory signals. Deploy- ment remains single-head and incurs no inference overhead.
Across nine solar-flare tasks, EVEREST achieves strong TSS (e.g., C: -.973/0..70//4 24/48/72 M5: 0.907 0.907/0.9330 00..36600.666 with well-calibrated probabilities (e.g.. M5-72h ECE = 0.016). The same backbone transfers unchanged SKAB with F1-98.16%, TSS=0.964, sur- passing published baselines (ilooovv et al. 2020 Tuli et al. 2022). Ablations attribute gains to temporal focusing (+0.427 TSS), EVT tail emphasis (+0.285). and evidential calibration (+0.064). Interpretability analyses show attention concentrating on physically meaningful precursors, and prospective X9.3 case study demonstrates early, well-calibrated alerts. Training efficient (814k params, AMP-enabled), supporting practical deployment.
Limitations. Our study inherits several constraints: (i) a fixed context window, which may miss very slow precursor dynamics; (ii) data gaps and quality filters that reduce effective coverage; (iii) potential cycle-dependent drift between training and deployment periods; (iv) extreme scarcity of the highest-mannitue events (e.g., X-class), limiting tail fitting and evaluation; and (v) unimodal inputs image and radio modalities are not considered here.
Future work. Promising directions include (i) streaming/State-sspace memory compressive transformers for indefinite context; (ii) multimodal fusion (e.g.. SHARP EUV/radio) with cadence- aware aligmment: (ii) federated or continual training mitigate cross-cycle drift and institutional data silos; (iv) model compression (guaatisationndiiiiillatoo and hardware-aware compilation for edge/ops deployment; and (v) richer time-series XAI (counterfactuals, TS- TS-IG) to strengthen opera- tional trust and post-hoc auditing.
Broader impacts. Reliable, calibrated, and tail-aware rare-event forecasts can improve risk com- munication and decision-making in high-stakes domains (e.g., space weather, industrial monitor- ing, power systems). EVEREST emphasises umall-moder efficiency and mixed-precision training, maintaining "Green AI" footprint while providing actionable probabilities and threshold analy- ses. We provide an anonymized artifact (code and splits) to support transparent benchmarking and reproducible research.

---

REPRODUCIBILITY STATEMENT
Code to reproduce all experiments provided in the Supplementary Material, includ- ing an anonymized repository with README.md, requirements. txt, and ready-to-run and SKAB
scripts for solar flares (models/train.pp) socdelsstrair__ete skab. py, models/ reveelsevaluatee skab. py). The archive includes the ex- act processed train/validation/test splits, configuration files, and evaluation routines used to report results. Runs use five fixed seeds, mixed precision (AMP), AdamW, cosine learning-rate decay, gradient clipping, and deterministic cuDNN settings; thresholds are selected via grid sweep and metries include TSS, Brier score, and ECE with 15 equal-frequency bins. Environment versions are pinned in requi rements txt, enabling end-to-end replication.
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Table 2: Per-module parameter and FLOP budget for EVEREST (FP32 multiply-adds; 7=10,
F=9, batch=1),
Module
Params (k)
FLOPs (M)
Embedding positional encoding
1.54
0.03
Transformer encoder x6
794.88
16.24
Attention bottleneck
0.13
0.00
Classification head
16.64
0.34
Evidential (NIG) head
0.52
0.01
EVT (GPD) head
0.26
0.01
Precursor head
0.13
0.00
Total
814.10
16.63
Table 3: Complexity comparison with SolarFlareNet (7=10, F=9, batch= 1).
Model
Params (k)
FLOPs (M)
FLOPs/ Param
SolarFlareNer (Abduallah et al. 2023)
6 120
0.62
0.10
EVEREST
814
16.6
20.4
A COMPLEXITY PROFILE
All numbers refer to single forward pass with T=10 time steps, F=9 SHARP features, and batch size
Per-module budget. The six-layer Transformer backbone accounts for the vast majority of pa- rameters and computation, with 794.9k 814.1k trainable weights (97.6%) and 16.24M of 16.63M FLOPs (97.7%) Each backbone weight is thus used about 20.4 times per inference. The auxiliary heads (evidential, EVT, precursor) together contribute only 0.91 parameters (0.11%) and 0.02M FLOPs (0.12%)
Cross-model comparison (SolarFlareNet). We compare EVEREST against SolarFlareNel (Ab- duallah et al. 2023) under the same input shape and profiling settings.
The reference architecture above underpins all reported experiments; tions, and evaluation protocols align with the modules and objectives in Section
ranges, abla-
B DATASET AND PRE- PRE-PROCSSSING
Pipeline. Our data pipeline builds on Abduallah et al. (2033), enhancing temporal fidelity (12- minute cadence), enforcing stricter quality masks, and version-controlling all outputs. SHARP vec- tor magnetograms (SDO/HMI) are merged with GOES flare data (NOAA/SWPC), programmatically harvested (JSOC, SunPy HEK), and segmented into supervised, HARPNUM-stran stratiifed windows.
Features. Nine SHARP parameters retained from the original 25, following physical inter- pretability and prior studies (Abduallah et al.) 2023). Table lists the features.
Split strategy. The mission window spans May 2010-May 2025. We create datasets for nine tasks (three flare thresholds x three horizons). Each HARPNUM appears in exactly one split. Table 5 gives the per-class distribution.
C SKAB INDUSTRIAL ANOMALY BENCHMARK
To assess cross-domain transfer, we evaluate EVEREST on the Skoltech Anomaly Benchmark (SKAB) (Filonov et al. 2020), suite of multivariate valve-sensor traces with fault events.
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Table 4: Selected SHARP features and their physical motivations.
Feature
Description
Physical motivation
TOTUSJH
Total unsigned current helicity
Magnetic twist; non non- con-potentiality
TOTPOT
Total magnetic free energy density
Energy reservoir for reconnection AR size / activity
USFLUX
Total unsigned flux
MEANGBT
Gradient of total field
Localised magnetic complexity
MEANSHR
Mean shear angle
Shearing near PIL
MEANGAM
Mean angle from radial
Loop inclination
MEANALP
Twist parameter a
Field line torsion
TOTBSQ
Total field strength squared
Energetic capacity
R_VALUE
PIL integral
Complexity near polarity inversion
Table 5: Number of positive and negative examples per flare class and horizon.
Flare
Horizon
Split
Positives
Negatives
24h
Train
244,968
218,217
Test
31,897
15,878
48h
Train
316,149
301,714
Test
40,987
21,573
72h
Train
356,219
350,853
Test
46,066
25,663
M
24h
Train
13,989
449,196
Test
1,368
46,407
48h
Train
16,709
601,154
Test
1,775
60,785
72h
Train
18,505
688,567
Test
2,131
69,598
M5
24h
Train
2,125
461,060
Test
104
47,671
48h
Train
2,255
615,608
Test
104
62,456
72h
Train
2,375
704,697
Test
104
71,625
We adopt the standard windowing (24 steps, stride two), stacked raw+diff channels, chronological 70/15/15 splits, and standardisation fitted train only. No oversampling additional task-specific loss reweighting is used; we reuse the same focal-los, configuration as in the solar-flare flare experiments. Architecture and loss weights are unchanged except for reduced width (d=96) and depth (L=4) to match the smaller dataset scale.
Results. Table o reports mean performance across all eight valve scenarios. EVEREST achieves strong discrimination (TSS 0.964 + 0.028) and calibration, with Fl exceeding 98%.
Comparison with baselines. T our results against prior published methods. EVER- EST surpasses the strongest reported baseline (TranAD (Tuli et al. (022)) by roughly Fl points, without task-specific tuning.
Protocol alignment and evaluation details. To align with prior work such as TranAD (Tuli al., 2022), we follow a standardised SKAB processing protocol. Each trace is converted into 24-step sliding windows (stride for training. full coverage at test time), using raw sensors together with 16 firs-diifference velocity features. A window is labeled anomalous if the first time step is annotated as a fault, yielding an early-event detection setting consistent with TranAD. All features standardised using training-set statistics only. At test time, fixed probability threshold of 0.5 applied; all metrics (TSS, FI, precision, recall) are computed at this threshold without post-hoc tuning.
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Table 6: EVEREST averaged across all SKAB valves.
Metric
Precision (%)
Recall (%)
F1 (%)
TSS
EVEREST
97.7 2.9
98.6 + 3.2
98.2 + 1.7
0,964 0.028
Table 7: F1 comparison SKAB valve anomalies.
Model
Reference
F1
Isolation F, LOF, etc.
Filonov al.
2020
65-75
Autoencoder
Filonov et al.
2020)
70-80
CNN/LSTM hybrids
Filonov al.
(0020)
75-85
TAnoGAN
Bashar & Nayak
[2020
79-92
DeepLog
Du ct al. (2017)
87-91
LSTM-VAE
Park al.
(2018
86-93
OmniAnomaly
Su et al. (0199
88-94
USAD
Audibert et (2020)
89-95
TranAD
Tuli et al.
(2022
91-96
EVEREST
98.2 + 1.7
Aggregate per-valve metrics (Valve 1). Table reports the aggregate confusion matrix and de- rived metrics for Valve I, combining all available Valve scenarios into single evaluation (micro- averaged across 12,500 test windows). This provides transparent, per-valve view while avoiding scenario-level redundancy.
Reproducibility. The full SKAB preprocessing and evaluation pipeline (windowing, normal- isation, chronological splits, and metric computation) is available in the released code under reproducibi dliitydatta /SKA///EDADDR md, including scripts for generating aggregate per- valve confusion matrices.
D HYPER-PARAMETER OPTIMISATION
Operational deployment values three traits above all: forecast skill, probabilistic reliability, and inference latency. We therefore tune only the hyper-parameters that collectively maximise skill x latency"
Method synopsis. We run a three-stage Bayesian study (Optuna v3.6 Ray Tune) over six knobs: embedding width d, encoder depth L, dropout p, focal exponent Y, peak learning rate Maaax and batch size B. Median-sten stopiingtooping pruning halves the number full trainings needed. Sobol sensi- tivity scan (Appendix [.1) confirmed that these six knobs explain 91 % of the variance in validation TSS.
Search logistics. Each fare-class/lead-tin time pair receives 166 trials split into exploration, refine- ment, and confirmation phases; exact budgets and early-stop criteria are in Appendix D.2
Final tuning space and winner. Table summarises the priors and the final configuration adopted for all production models. Full per-scenario optima are in Appendix D.4
The selected tuple (d=128, L=6, 2= p=0.20, 7max 4x 10 0.795 + 0.005 and inference latency of + 0.6 on an NVIDIA RTX 6000. These values are frozen for all ablations and the compute-budget audit.
10-4, 4 B=512) achieves TSS
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Table 8: Aggregate results for SKAB Valve (micro-averaged across all Valve 1 scenarios).
TP
FP
TN
FN
Precision (%)
Recall (%)
TSS
5694
134
6591
81
97.7
98.6
0,966
Table 9: Search priors and final hyper-parameters used production
Hyperparam
Prior
Rationale
Best
Embedding d
[64, 128, 192, 256} [4. 6.
capacity vs. latency
128
Encoder depth L
receptive field over-fit control
Dropout p
4/0.05, 0.40] U11,4]
0.20
Focal ?
minority gradient
2.0
Peak LR Tmaax
Log-U[22x10-4 8x10
step size throughput vs. generalisation
4x10
Batch size B
{256, 512, 768, 1024]
512
D.1 SOBOL SENSITIVITY SCAN
64-trial Sobol sweep assessed first-order and total-order effects; the six retained knobs jointly explain 91 % of variance in validation TSS. Full indices and code are in the repository.
D.2 SEARCH PROTOCOL
Each study followed the three-stage schedule in Table I0, Trials were pruned with Optuna's 's median rule after five epochs.
D.3 HYPER- PAEER-PARAMEEERR RATIONALE
1. Capacity: d and govern receptive field and FLOPs.
2. Regularisation: p mitigates over-fit.
3. Imbalance: addresses the 1 1:297 positive/negative ratio.
4. Optimiser dynamics: Mmax sets AdamW step size.
5. Throughput: B trades GPU utilisation for generalisation.
D.4 PER TER-SCENARIO OPTIMA
Table 11 lists the best trial for each of the nine studies.
clear pattern emerges: C and classes share a single optimum across all windows, while M5
requires larger capacity for short horizons and deeper, narrower networks for 72h forecasts.
D.5 ADDITIONAL DATA PRE-PROCESSING VISUALS
* CMD filtering diagram (Fig. 2 effect of the ICMD| < 70 mask on the usable sequence pool during solar data pre-processing.
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Table 10: Trial budget per stage for each flare-class/ead-tiie study
STAGE
TRIALS EPOCHS/TRIAL
PURPOSE
Exploration
120
20
Global sweep of parameter space
Refinement
40
60
Focus top-quartile region
Confirmation
120
Full-length convergence check
Table 11: Best hyper-parameter, per flare class and forecast window
FLARE
WINDOW
L
7maax (10 (10-4)
TIME (s)
C
24h
128
4
0.353
2.803
5.337
512
3323
48h
128
4
0.353
2.803
5.337
512
4621
C
72h
128
4
0.353
2.803
5.337
512
4856
M
24h
128
4
0.353
2.803
5,337
512
3705
M
48h
128
4
0.353
2.803
5.337
512
5105
M
72h
128
4
0.353
2.803
5.337
512
5871
M5
24h
192
4
0.300
3,282
4.355
256
3778
M5
48h
192
4
0.300
3,282
4.355
256
4977
M5
72h
64
8
0.239
3,422
6.927
1024
5587
E EXTENDED RESULTS AND PROTOCOLS
E.1 EXPERIMENTAL PROTOCOL
We evaluate nine benchmark tasks (three flare thresholds: C, M, M5; three horizons: 24h, 48h, 72h). Performance statistics are computed via 10,000-fold bootstrap resampling with splits stratified by NOAA active-region identifier to avoid temporal leakage. Each task is trained and evaluated with 5 random seeds; metrics are aggregated as mean (standard deviation) unless otherwise noted. Thresholds are selected by balanced scoring rule over grid of 81 values in [0.1, 0.9) (step 0.01), with a fallback with minimum effect size threshold ATSS 2 0.02.
if no improvement is found. Statistical significance is assessed at p < 0.05
E.2 BOOTSTRAPPED METRICS (FULL)
Table 12 reports bootstrapped performance the held-out test set for all nine tasks (higher is better for TSS/Precisson/Recall: lower is better for Brier/ECE)..
E.3 SIGNIFICANCE VS. BASELINE
We compare against the strongest baseline (Abduallah et al. (023). Improvements are significant at p < 0.01 for all nine tasks (Table 13] bootstrap hypothesis testing).
E.4 CALIBRATION AND OPERATING POINTS
Reliability diagrams (15 squal-frequenyy bins) and cost-loss analyses are provided for represen- tative tasks (figures referenced in the main text). Operating thresholds 7" for each task the grid-search optima under the balanced scoring rule; values are available in the code repository and summary tables.
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Figure Central-meridian -isiaian-distance (CMD) quality mask applied to an HMI synoptic magne- togram. The bright-green curve marks the acceptance limit [CMD] 70;; grey wedges beyond this boundary are discarded. Active-region boxes are color-coded by the centroid rule: green outlines (e.g., AR 12263, 12266) fall inside the limit and are retained, whereas red outlines (e.g., AR 12267) lie outside and are excluded. The mask removes limb data affected by foreshortening and line-of- sight artifacts while preserving the central disk used for training and evaluation.
Table 12: Bootstrapped performance (mean + 95% CI) of EVEREST on the held-out test set.
Thresholds are the task-specific optima from the balanced scoring rule.
Task
TSS
Precision
Recall
Brier
ECE
C-24h
0.973 + 0.001
0.994 + 0.000
0.986 0.001
0.015 + 0.000
0.049 0.000
C-48h
0.970 0.001
0.993 + 0.000
0.984 0.001
0.017 = 0.000
0.054 0.000
C-72h
0.966 + 0.001
0.992 + 0,000
0.982 + 0.001
0.018 = 0.000
0.052 + 0,000
M-24h
0.898 + 0.011
0.728 = 0.016
0.908 0.011
0.011 0.000
0.037 0.001
M-48h
0.920 0.007
0.772 + 0.010
0.928 + 0.007
0.009 0.000
0.029 + 0.000
M-72h
0.906 0.012
0.834 + 0.015
0.911 0.012
0.010
+ 0.000
0.033 0.001
M5-24h
0.907 0.025
0.686 0.033
0.908 + 0.025
0.003 + 0.000
0.031 + 0.000
M5-48h
0.936 0.021
0.713
+ 0.035
0.937 0.021
0.002
+ 0.000
0.020 0.000
M5-72h
0.966 - 0.024
0.727 + 0.053
0.966 - 0.024
0.002 = 0.000
0.016 0.000
F ADDITIONAL CALIBRATION PLOT
CLASS-CONDITIONAL CALIBRATION
To assess whether global calibration obscures class-specific effects, we compute reliability diagrams separately for the negative and positive classes on the M5-72h task using 15 equal-frequency bins. Figure shows the resulting curves, based on n=71,625 non-flaring windows and n=104 flaring windows.
Findings. Negative-class calibration excellent = reflecting that the model consis- tently assigns low probabilities non-flaring windows. Positive-class calibration exhibits higher variance (ECE=0,4236), which expected in the samples per bin. Importantly, the positive-class curve remains monotone and close to the diagonal at higher probability levels, indicating that high-confidence flare forecasts correspond to genuinely flaring cases.
regime with very few flaring
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Table 13: Statistical significance of TSS improvements the strongest baseline (Abduallah
al. 2023). EVEREST values are mean (95% CT) from 10,000 bootstrap resamples stratified by HARPNUM. Asterisks denote bootstrap p-values for the null Ho ATSS S 0: 0.05, *
< 0.01, p < 0.001.
Task
Baseline TSS
EVEREST TSS
Effect size ATS
C-24h
0.835
0.973 (0.001)
+0.138***
M-24h
0.839
0.898 (0.011)
+0.059**+
M5-24h
0.818
0.907 (0.025)
+0.089****
C-48h
0.719
0.970 (0.001)
+0.251 #**
M-48h
0.728
0,920 (0.007)
+0.192 #*
M5-48h
0.736
0,936 (0.021)
+
C-72h
0.702
0,966 (0.001)
+0.264 WMW
M-72h
0.714
0.906 (0.012)
+0.192
M5-72h
0.729
0.966 (0.024)
+0.237 #
M5-72h (71,729 samples, 104 positive)
1.0
Gas:: 0.243
Pertect calibrtton
0.0
0.2
0.4
0.6
0.8
10
Mean Predicted Probability
Figure 3: Reliability diagram for the M5-72 task. Shaded region shows 95% bootstrap confidence intervals; the dashed line indicates perfect calibration. ECE = 0.016 with maximum bin gap 0.263.
F.2 HIGHI-CONFIDECEE (p 0.8) CALIBRATION
Operational forecasting places particular emphasis on reliability in the high-alert region. We there- fore analyse calibration conditional on á > 0.8 for the M5-72h model. This region contains n=8 test windows, all corresponding to true M5 flares.
Findings. All high-confidence predictions are correct (100% precision), with predicted probabili- ties concentrated between 0.80 and 0.83. The resulting ECE (0. (0.1881) reflects the small sample size rather than systematic miscalibration. These results show that the model is highly trustworthy in the operationally critical high-alert regime, issuing confident predictions sparingly but accurately.
G CONFUSION MATRIX ANALYSIS UNDER AS ASYMMETRIC COSTS
The confusion matrices below quantify the effect of selecting different operating thresholds the M5-72h task. At the balanced-score threshold (T = 0.460), the model achieves strong overall discrimination but incurs some false negatives. At the cost-minimising threshold (r* 0.240), all false negatives are eliminated at the expense of more false positives.
19

---

Confidance
Prediction
1.3
cofferes nen pnot (EC--p00 Perfer coloratto
(ym204)
Dediton socareld
Obrorvd Resurncy
10'
Clire
100
30F
0.2
2.5 Prodicted Probabiltt
2.4
0.0
0.2
9.5
Prodicted Probabilty
Figure 4: Class-conditional calibration for h5-72h. Left: negative-class reliability curve (FCR=0.0097).. Right: positive-class reliability curve (CCE=0.4236) Higher positive-class ECE reflects the extreme rarity of M5 events, but the curve remains monotone and near-diagonal high predicted probabilities.
High-Risk Risk Segime (p=-08)
High-Risk Prediction Distribution samples p=-088
68%
3.0
Threshod 2-0.0
Nated
2.0
1.0
No haae
0.52
0.03
0.00
0.05
0.56
0.67
0.800
0.005
0.810
0.015
0.625
Predicted Probability
Predicted Probabilty
Figure 5: High-confidence calibration (p > 0.8) on h5-72h. Left: observed vs. predicted fre- quencies for all high-confidence samples (all eight true flares). Right: histogram of predicted probabilities in this regime. Despite the small sample size, the high-alert region exhibits perfect precision, indicating reliable operational behaviour.
T55-Optimized Threshold 0.460)
Cost-Op Optt-ippimized Threshold (r* 0.240)
70200
70000
Proca 672
Necikee 8.475
72582
50000
53000
-0300
40200
20200
30000
Fiee
13300
10000
Hure
Predicted
Predicted
Figure 6: Confusion matrices for the M5-72h model. Left: balanced-score threshold T 0.460 (92 TP, 45 FP, 71,580 TN, 12 FN). Right: cost-minimising threshold r" 0.240 (104 TP, 115 FP, 71,510 TN, O FN).
20

---

Table 14: EVEREST ablation results M5-72h (mean + s.d. over 5 seeds).
VARIANT
TSS
F1
BRIER
ECE
p
Full model
0.746 + 0.146
0.747
0.0013
0.0110
No Evidential head
0.682 + 0.193
0.626
0.0015
0.0111
< 0.01
No EVT head
0.461 = 0.369
0.438
0.0039
0,0336
< 0.01
No Evidential EVT heads
0.640 = 0.275
0.594
0.0015
0.0115
< 0.01
Mean pooling
0.319 = 0.319
0.304
0.0229
0.1158
< 0.001
Cross-entropy loss
0.209 = 0.332
0.195
0.0013
0.0023
0.001
No Precursor head
0.096 + 0.174
0.095
0.0194
0.1105
< 0..01
FP32 training
0.000 + 0.000
0.000
0.0520
0.2248
0.001
Table 15: Component ablation on M5-72h.
Paired bootstrap (10* replicates) vs. full model.
COMPONENT REMOVED
ATSS
REL. CHANGE (%)
p-VALUE
Mixed Precision (AMP)
-0.746
-100
< 0.001
Precursor head
-0.650
-87
< 0.001
Focal loss
-0.537
-72
< 0,001
Attention bottleneck
-0.427
-57
< 0.001
EVT head
-0.285
-38
< 0.001
Evidential head
-0.064
-9
0.004
H ABLATION STUDY SUITE
We ran systematic crve-oonecommooenn-ouu protocol with five seeds per variant to quantify the contribution of each EVEREST module. All runs targeted M5-class flares 72 h horizon (the hardest task), with identical data splits, early stopping at 120 epochs, and bootstrap evaluation (10* replicates). This fixed training schedule is shorter than the full HPO-tuned training used for the headline results in Table T2 and therefore the absolute TSS of the "Full model" reported here is lower; however, relative ATSS across variants directly comparable. Tables and [55 mean metrics, effect sizes, and significance relative to the full model under this standardised protocol.
Interpretation. Four findings stand out: (i) mixed precision numerically indispensable (FP32 diverged); (ii) the precursor auxiliary is the strongest regulariser, preventing collapse under extreme rarity: (iii) the attention bottleneck far outperforms mean pooling, (iv) evidential and EVT heads play complementary roles, with the former reducing calibration eπror and the latter improving tail- sensitive diserimination. These results support the design hypothesis that each module addresses a distinct failure mode.
21

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H.1 LOSS-TERM ABLATIONS (OBJECTIVE-LEVES)
To complement the module-wise architectural ablations, we also evaluate loss-term ablations iso- lating the effect of each component of the composite objective. Concretely, we retrain EVEREST while removing: (i) the evidential NLL term (A, = 0), (ii) the EVT exceedance penalty (Aę = 0), and (iii) the precursor BCE term (Ap 0). The backbone and training setup remain identical.
These variants correspond exactly to "No Evidential head", "No EVT head", and "No Precursor head" in Table 14 since the auxiliary heads contribute only through their loss terms during training and are removed at inference.
Removing each loss component produces characteristic degradation patterns:
No evidential NLL increases ECE while preserving moderate TSS, confirming it acts pri- marily as calibration regulariser.
No EVT loss sharply increases tail-region Brier score and reduces TSS, showing that the EVT term shapes extreme-value discrimination.
No precursor BCE causes the steepest drop in TSS, indicating that the anticipatory super- vision stabilises learning under severe rarity.
We also observe that the largest gains from the EVT penalty occur specifically in the highest- probability, highest-flux windows (top 10% of the predictive distribution). i.c., the regime used for the tail Brier score, indicating which samples benefit most from extreme-value regularisation. Figure 7 shows calibration curves for these three variants compared the full model, demonstrating that each loss term targets a distinct aspect of the predictive distribution.
H.2 X-SENSITIVITY OF EVIDENTIAL AND EVT LOSSES
To assess whether the auxiliary loss weights behave as robust regularisers rather than fragile knobs, we ran 5x5 sensitivity grid over the evidential and EVT loss terms on the M5-72h h task. Let Xevid and Xeve denote the default weights from Section 3 we form Aevid = Revid-Aivid and Aevs = Kevt Agvt with multipliers Kevid: Kevt E {0.5. 0.75, 1.0, 1. 1.25, 1.5). The backbone, focal schedule, optimiser, and early- stolyigg protocol are held fixed.
H.3 EVT QUANTILE SWEEP
To assess sensitivity to the EVT exceedance threshold, sweep the quantile E (0.85, 0.90, 0.95) for the M5-72 task, keeping all other hyper-parameter, fixed (including λevi). For each u we train two seeds with identical splits and evaluation protocol as in the main experiments. In addition to global metrics, we compute:
a tail Brier score restricted to the top 10% of predicted probabilities (7,174 windows), and a mid-range Brier score on the remaining 90% of windows (64,555 windows).
Table 16 reports the range (min-max) across the two seeds for each quantile. Across all three settings, performance varies only within seed-level noise: TSS remains between 0.9032 and 0.9319, ECE between 0.0119 and 0.0164, and the tail Brier score between 0.01568 and 0.01718. Mid-range Brier stays on the order of 10 in all cases. This indicates that the EVT head acts as robust tail-regulariser rather than fragile knob tuned single threshold.
H.4 FOCAL-LOSS SCHEDULE SENSITIVITY
The focal-loss exponent 7 is annealed from 0 to 2 during the first 50 training epochs (Section 3 To assess whether this schedule constitutes fragile hyperparameter, we compare four variants on the M5-72h h task, keeping all other settings fixed:
1. Constant 2: no annealing; the focal exponent is fixed at 2 from initialization.
2. Standard schedule (baseline): y increases linearly from 0 to over the first 50 epochs (used in all main experiments).
22

---

Evidaniall
EVT
Cel brstit Dur
Cabrucccoinn caree
- Colbration curE
0.2
Obsgevad mesqaaee
Observd Frequeoyy
0.5
0.5
Observed
0.4
0.2
0.3
0.0
0.0
0.2
2.4
0.5
08
0.2
0.4
0.6
0.5
1.0
0.0
0.9
0.2
0.4
09
Prodicted boo
Prodicted Prbbab Biy
No Precurmet
Evil-EVT LowN.
1.0
2.0
Porkec cfl trrtion
Pertec brotes
0.5
2.5
0.5
5.8
Obseredd
0.4
0.4-
0.2
5.-
0.0
3.4
Pradicted Prozabilty
Pred ctod Probab Byy
Figure 7: Calibration curves for loss-term ablations on the M5-72h task. Each panel shows the reliability diagram (15 equal-frequency bins) for the full composite loss (top-left) and variants with individual loss terms removed. Removing the evidential NLL (No Evidential Loss) primarily de- grades mid-range calibration, consistent with its role in regularising logit uncertainty. Removing the EVT exceedance penalty (No EVT Loss) disrupts tail calibration, producing overconfident pre- dictions in the high-probability regime. Eliminating the precursor supervision (No Precursor Loss) causes systematic underconfidence and curve flattening, reflecting reduced early-signal shaping in the shared backbone. Joint removal of the evidential and EVT penalties (No Evid+EVT Loss) yields the most unstable reliability curve, confirming that the two losses have complementary but non- redundant effects. Together, these curves demonstrate that each loss component targets a distinct calibration failure mode, and that the full composite objective yields the most reliable probabilistic forecasts.
Table 16: EVT quantile sweep on h5-72h. For each quantile we report the range (min-max) of global TSS, ECE, and tail Brier score across two random seeds. Tail Brier is computed on the top 10% of predicted probabilities.
Quantile
TSS (min-max)
ECE (min-max)
Tail Brier (min-max)
0.85
0.9032-0.9284 0.9065-0.9319 0.9047-0.9276
0.0121-0.0164 0.0119-0.0158 0.0123-0.0160
0.01568-0.01712 0.01572-0.01705
0.90
0.95
0.01718
3. Higher target (7 = 4): increases linearly from O to 4 over 50 epochs.
4. Lower target (y 1): y increases linearly from to 1 over 50 epochs.
Table 17 reports TSS and ECE for these variants the test set (mean + s.d. over 5 seeds). All configurations with 7 E 22, 4] achieve strong performance (TSS > 0.90), with negligible perfor- mance differences between the constant-y 2 and annealed 0-+2 schedules. The lower-target schedule (y = 1) exhibits pronounced training instability and degraded discrimination, confirming that sufficient re-weighting strength is necessary under severe imbalance.
23

---

TSS Sensibivity
ECE Sensitivity
0.74
1.25
Leeii multiplle
3. L.viid multiplles
9
8010
0.75
1.0
1.25
1.5
0.5
0.75
1.25
multipiier
(a) TSS sensitivity. TSS varies only within 0.70-0.75 across the (Kevid, Kevt) grid, with mild dependence on Revid negligible sensitivity to Kevt. indicating that both auxiliaries act as stable regularisers.
(b) ECE sensitivity. Calibration error remains in the 0.010-0.018 range for all weight combinations, showing that EVEREST maintains good calibration even under wrder-of-magnituee changes to auxiliary loss weights.
Figure 8: Sensitivity of EVEREST to the auxiliary loss-weight multipliers (Kevidd kevv) on the M5- 72 h task. Across the 5x5 grid, both discrimination (TSS) and calibration (ECE) vary only mildly, indicating that performance is stable over a wide range of evidential and EVT loss weights.
Table 17: Effect focal-loss exponent schedules on M5-72 (test set, mean s.d. over 5 seeds).
Schedule
TSS f
ECE +
Constant 7 = 2 (no anneal)
0.985 + 0.008
0.008 + 0.001
Standard (0->2 over 50 epochs)
0.951 0.013
0.008 + 0.002
Higher target (0-+4 over 50 epochs)
0.918 + 0.047
0.043 + 0.005
Lower target (0-+1 over 50 epochs)
0.788 0.245
0.003 + 0.000
These results suggest that EVEREST is not sensitive to the precise focal-loss schedule: both the constant-y variant and the standard annealed schedule yield high TSS with low ECE, while only very small exponents (y > 1) substantially harm rare-event discrimination. The focal term thus acts robust imbalance regulariser rather than finely tuned knob requiring delicate annealing.
I GRADIENT-BASED INTERPRETABILITY
We visualise feature saliency gradients for representative tasks probe the signals driving EVER- EST predictions. Figure 9 summarises average gradient evolution across true positives (TP), true negatives (TN), and false positives (FP). Distinct temporal morphologies emerge: TP cases show sustained positive gradients in USFLUX and MEANGAM, while TN and FP cases lack such coherent rises.
To examine how predictive confidence aligns with saliency signals, Figure 10 shows TP cases
stratified by model confidence. High-confidence TPs exhibit the strongest multi-feature gradients, whereas low-confidence TPs show weaker but still consistent rises.
24

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16H90X
AUSA
LAWKS
RAB
9040
RAB
14.00
vore
160
4000
4080
KLAM
HUMM H4.0
AMM
4.44
0.00
1644
6.0 TAMK
Figure 9: Feature evolution heatmaps across prediction outcomes (True Positive, True Negative, False Positive). Coordinated increases in USFLUX and MEANGAM appear TPs, while TNs and FPs show flatter or noisier profiles.
TP Casac Gradient Analydic by Confidence Level (MI Canfidenco Levelc)
High
JAME
JAM
-1M
90007
JAM
Lovn Ceaf Teac)
AM
4MM
9000
90:0
Figure 10: Gradient evolution for True Positive (TP) M5-72h predictions stratified by model confi- dence. Strongest gradients appear in high-confidence cases.
25

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September X9.3 Solar EVEREST Model Performance with 72h Roiling Predictions
Kieten Hiw Ritiaally
dunt Thapkied
Thvarddd
iicasillyy staas
Lead Times 2.7h
sheee leed
48
RV5T
19400
Time (UTC)
Figure 11: Prospective replay of the 6 September 2017 X9.3 flare. Blue: EVEREST M5-72h probability (with 95% interval, if shown). Dashed lines mark alert thresholds (10%, 20%, 46%); grey shows GOES soft X-ray flux.
Table 18: Lead-time statistics for EVEREST (M5-72h) on the 6 Sep 2017 X9.3 flare.
Threshold (7)
First crossing (UTC)
Lead time Continuous alert length
10%
04 Sep 00:57
59.3 h
60.8 h
20%
04 Sep 14:01
52.5 h
53.6 h
46%
06 Sep 09:19
2.7 h
2.3 h
J PROSPECTIVE REPLAY: 6 SEPTEMBER 2017 X9.3 FLARE
The X9.3 flare of 6 September 2017 (NOAA AR 12673, peak at 12:02 UT) was held out from train- ing and threshold calibration (3-7 Sep 2017) to provide a true out-of- sample test. Figure 11 shows the M5-72h h probability trace; Table 18 lists the associated lead times for several alert thresholds.
K
NOTATION
Symbol
Description
X YERIX
Input window (length T, F features)
y E [0,1]
Binary rare-event label
H (1 Ji=1
Hidden states after encoder layer
2 E Rdd
Pooled representation from the attention bottleneck Classification logit; ş o())
(u, 8)
NIG parameters of the evidential head over /
(5,0)
GPD tail parameters for EVT exceedance modelling
High--qunntile threshold for defining exceedances
Loss weights (focal evidential EVT precursor) Decision threshold on á for issuing alert
Table 19: Main notation used in Sections
26

---

Method
Long-range
Calib.
EVT
Liu al. (2019) LSTM Sun et al. (2022) 3D-CNN SolarFlareNet (2023) EVEREST (ours)
Short memory
No
No
Local convs
No
No
CNN attention
No
No
Transformer + bottl.
Yes
Yes
Table 20: Baseline capability comparison.
L BASELINE ARCHITECTURES AND REPRODUCTION DETAILS
We benchmark against the three strongest published SHARP GREE forecasting models with avail- able results code: an LSTM forecaster, 3D CNN, and NolarFlareNet (CNN-Transformer hy- brid). These represent the major architectural families used in prior flare prediction work.
[Luu et al. 2019 LSTM forecaster. stacked LSTM (2-3 layers, 256 units) applied to SHARP parameter sequences, followed by fully-connected classifier. Each window is processed independently (no cross-window recurrence), and the network outputs probability of a 2C/M/M5 flare within the horizon.
Sun ef al. 2022) - 3D Convolutional Model. A 3D-CNN originally designed for patch-based magnetogram cubes. We follow the published configuration: 3D conv blocks + temporal pooling linear head. Since only SHARP parameters are available (not full magnetograms), we adopt the authors' SHARP-mode variant reported in their supplementary results.
Abduallah et al.,
2023 SolarFlareNet. A -CNN-Transformer hybrid with spatial convolutions
followed by global self-altention and an MLP classifier. This is the strongest published baseline; we use the publicly reported hyperparameters and the same HARPNUM-stratifedd splits.
Training and evaluation. All baselines are retrained using the same HARPNUM stratification, SHARP features, normalization, cadences, thresholds, seeds, and evaluation metrics EVEREST. Threshold selection follows the same balanced-score rule.
27


Reviews:
[{"review_id": "ZGgYWDAPhu", "reviewer": "Reviewer_2G8S", "summary": "The authors \n\nThe authors claim the following contributions\n1. A recipe that focuses on long contexts via a single-query attention bottleneck, \n2. A recipe that learns calibrated, closed-form uncertainty (evidential NIG on the logit), and \n3. A recipe that emphasises extremes through an EVT exceedance penalty", "questions": "None", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "# originality\n\nTests on two benchmark datasets\n\n# significance\n\nResults show the method performs well on the benchmarks", "weaknesses": "# clarity\n\nThe paper is quite challenging to evaluate. It reads in a stop-start fashion, lacking flow.\n\nIt feels like the paper presents a Rubik's Cube solution that has been heavily tuned to the specific datasets. For instance, the method introduced four new hypers in the loss.\n\nI find it difficult, in its current form, to determine whether the method presents an approach that is generalizable beyond these specific case studies.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "f0eqmkt2ze", "reviewer": "Reviewer_gJ8E", "summary": "This paper introduces EVEREST, a Transformer-based architecture designed for forecasting rare events in multivariate time-series data. \n\nThe model's main proposition is to integrate four components: \n\n- a learnable attention bottleneck for temporal aggregation\n- an evidential head for uncertainty quantification \n- an extreme-value head to model tail risk \n- a precursor head for early detection. \n\nThese components are jointly optimized during training via a composite loss function, but only the primary classification head is used for inference, resulting in no runtime overhead. \n\nThe authors claim SoTA performance on the SHARP dataset and test cross-domain transfer to the SKAB dataset. Both datasets known for presence of rare events in time series.", "questions": "1. A more detailed ablation analysis and if possible correlating the components to the kind of samples they improve would be the one thing that would make the biggest difference to the quality of this work.\n\n2. A couple of relevant references that seem to be missing here are\n\nhttps://arxiv.org/abs/2202.13418: This work uses the pareto distribution to model extreme events in time series.\nhttps://arxiv.org/abs/2103.12474: Work exploring contrastive loss, could be an interesting direction to check as well", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The work presents some novelty of architecture and a combination loss to train it well. This is a valuable combination of many existing concepts often considered in the field. The strong performance of the trained models on the tested datasets shows the effectiveness of the approach. \n\nSome of the work in the appendix such as appendix J shows good real world utility of the methods.\n\nThe paper is well presented and the code and instructions made for reproducibility are super helpful. They help validate the information shared in the work.", "weaknesses": "The hardest to buy assumptions are about the multi-component loss. Section 5.4 very briefly talks about ablations to identify the value of each of these and there is explanation presented in Section 3.5 however it does not feel enough to motivate the key contribution of the work. It would be significantly more compelling to run better designed loss component ablations. Some analysis on whether each loss component and head are actually showing improvement on the kind of time series one would expect. Just looking at overall numbers in the case of evaluating such specific choices tends to obfuscate the true value of the contributions. This is the key reason behind my weak accept rating and not an outright accept.\n\nSuch analysis might also allows readers to gain insights on adapting these methods to other datasets and settings. \n\nThe complexity of navigating the hyperparameter landscape that this loss function introduces is also something that more discussion and analysis would help.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aBz3NIZowI", "reviewer": "Reviewer_vM41", "summary": "This paper introduces EVEREST, a compact Transformer-based architecture for probabilistic rare-event forecasting in multivariate time series, addressing core challenges of class imbalance, long-range dependencies, and distributional uncertainty. Targeting high-stakes domains (space weather, industrial monitoring), it delivers calibrated predictions, tail-risk estimation, and interpretability—with no inference overhead (814k parameters) as auxiliary modules act only at training.\n\nKey contents.\n\n1. EVEREST integrates four critical components:  a learnable attention bottleneck, an evidential (Normal-Inverse-Gamma) head, an EVT (Generalized Pareto) head, a precursor head.\n\n2. A unified objective balances discrimination (focal loss for imbalance), calibration (evidential NLL), tail awareness (EVT penalty), and anticipatory learning (precursor BCE).\n\n3.  The proposed method has the practical utility, compact (16.6M FLOPs), efficient to train (24s/epoch on RTX A6000), and interpretable (attention aligns with physical precursors like solar flux).", "questions": "1. I feel like the presentation is very poor. The organization of this article is really weird. All sections are structured as fragmented short paragraphs. This makes it more like an outline document rather than an academic paper.\n\n2. The presentation of Section 3 is not clear enough. For example, there is no explanation of symbols $T$, $F$. There are no formula definition of  $\\mathcal L_{evid}, \\mathcal L_{evt}, \\mathcal L_{prec}$.\n\n3. In the loss function, there are four tuning parameters, namely, $\\lambda_f, \\lambda_e, \\lambda_t, \\lambda_p$. Actually, only three of them are needed due to the scale invariance.\n\n4. Baseline methods like Liu et al 2019, Sun et al 2022, Abduallah et al 2023 are not well-explained.\n\n5. Overall, the poor writing prevents me to fully judge the quality of the paper.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "The paper has its own originality by addressing a longstanding gap in rare-event time-series forecasting: joint optimization of discrimination, calibration, and tail-risk within a compact, deployment-efficient framework—an unmet need in prior work that treated these goals in isolation. Unlike existing Transformer-based methods that prioritize discrimination over uncertainty or tail behavior, EVEREST innovates via four integrated, training-only auxiliary modules: a single-query attention bottleneck, an evidential Normal-Inverse-Gamma (NIG) head, an Extreme Value Theory (EVT) head, and a precursor head (anticipatory supervision). The proposed method is shown to achieve reasonably good experimental results.", "weaknesses": "The biggest weakness of this paper is its presentation. See detailed comments in \"Questions\" section.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wJJ7LI4mOB", "reviewer": "Reviewer_bt8K", "summary": "The paper proposes EVEREST, a compact rare-event time-series forecaster that co-optimizes discrimination, calibration, and tail-risk. The backbone is a 6-layer Transformer with a single-query attention bottleneck. Three training-only auxiliaries, including an evidential NIG head on the logit, an EVT (GPD) exceedance loss, and a lightweight “precursor” head, regularize the shared representation, while deployment uses a single classification head (≈0.81M params). On SHARP–GOES solar-flare benchmarks, EVEREST reports SOTA TSS across 9 tasks (e.g., ≥C: 0.973/0.970/0.966 at 24/48/72h) with strong calibration; it also transfers to SKAB with F1≈98%.", "questions": "- How sensitive are results to the exceedance quantile and to the stability regularizer? \n- Generalization under distribution shift: Any evaluation across solar cycles or on a temporally forward-held test period (e.g., 2024–2025 only) to assess drift? \n- Calibration granularity: Do you have class-conditional and operating-threshold–conditional calibration (e.g., reliability among high-risk alerts) beyond global ECE? \n- SKAB protocol alignment: Please clarify whether your SKAB setup strictly matches TranAD and others (windowing, labels, early-event detection vs point detection) and provide per-valve confusion matrices. \n- Ablation rigor: Could you report per-seed distributions for the large ∆TSS (e.g., +0.427 from the bottleneck) and include a joint ablation (evidential+EVT removed together)?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Tail-aware + calibrated: EVT loss on logits and evidential NIG improve both sensitivity to extremes and reliability (e.g., ECE≈0.016 on the hardest task). \n- Clear ablations & diagnostics tying gains to modules; decision-theoretic thresholding (cost–loss) is practical.", "weaknesses": "- Novelty is integrative rather than fundamentally new—readers may view EVEREST as a well-engineered recipe.\n- Data scarcity at the extreme tail (e.g., M5 with 104 positives/test) risks optimistic estimates; consider additional robustness tests (temporal hold-outs, cycle shift). \n- Sensitivity analysis limited: fixed EVT quantile ($\\mu$=0.9), $\\lambda$ weights, and focal-$\\gamma$ schedule. More systematic sweeps (or conformal variants) would strengthen claims.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)