Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring
Authors: Changhun Kim
Yechan Mun
Hyeongwon Jang
Eunseo Lee
Sangchul Hahn
Eunho Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Time Series
Online Time Series Monitoring
Explainable Artificial Intelligence
XAI
Primary area: interpretability and explainable AI

Abstract: Explaining online time series monitoring models is crucial across sensitive domains such as healthcare and finance, where temporal and contextual prediction dynamics underpin critical decisions. While recent XAI methods have improved the explainability of time series models, they mostly analyze each time step independently, overlooking temporal dependencies. This results in further challenges: explaining prediction changes is non-trivial, methods fail to leverage online dynamics, and evaluation remains difficult. To address these challenges, we propose Delta-XAI, which adapts 14 existing XAI methods through a wrapper function and introduces a principled evaluation suite for the online setting, assessing diverse aspects, such as faithfulness, sufficiency, and coherence. Experiments reveal that classical gradient-based methods, such as Integrated Gradients (IG), can outperform recent approaches when adapted for temporal analysis. Building on this, we propose Shifted Window Integrated Gradients (SWING), which incorporates past observations in the integration path to systematically capture temporal dependencies and mitigate out-of-distribution effects. Extensive experiments consistently demonstrate the effectiveness of SWING across diverse settings with respect to diverse metrics. Our code is publicly available at https://github.com/AITRICS/Delta-XAI.

Published as a conference paper at ICLR 2026
DELTA-XAI: A UNIFIED FRAMEWORK FOR EXPLAIN- ING PREDICTION CHANGES IN ONLINE TIME SERIES MONITORING
Changhun Kim!*
Yechan Mun Hyeongwon Jang"
Eunseo Lee
Sangehul Hahn Eunho Yang!.2
AITRICS KAIST $Sungkyunkwan University
I (changhun. kim, ycmun, s. sahan, eunhoy /aattiicstc..oo..e..ee.i.i com Sanghw09110kaist
dldmstj3116g skku.
ABSTRACT
Explaining online time series monitoring models is crucial across sensitive domains such as healthcare and finance, where temporal and contextual prediction dynamics underpin critical decisions. While recent XAI methods have improved the explain- ability of time series models, they mostly analyze each time step independently, overlooking temporal dependencies. This results in further challenges: explaining prediction changes is non-trivial, methods fail to leverage online dynamics, and eval- uation remains difficult. To address these challenges. we propose Delta-XAI, which adapts 14 existing XAI methods through a wrapper function and introduces princi- pled evaluation suite for the online setting, assessing diverse aspects, such as faith- fulness, sufficiency, and coherence. Experiments reveal that classical gradient-based methods, such as Integrated Gradients (IG), can outperform recent approaches when adapted for temporal analysis. Building on this, we propose Shifted Win- dow Integrated Gradients (SWING), which incorporates past observations in the integration path to systematically capture temporal dependencies and mitigate out- of-distribution effects. Extensive experiments consistently demonstrate the effec- tiveness of SWING across diverse settings with respect to diverse metrics. Our code is publicly available at https: /retps://giteee /ttpt//gttttt/tt//// com/ wtt.///AARI///eeeaaaa....
INTRODUCTION
Time series data, inherently gleaned online, span safety- and mission-critical domains such as healthcare (Johnson et al. 2016 Reyna et al. 2020p. transportation (Polson & Sokolov 2017 Zheng
Huang 2020), finance (Sezer et 2020 Xu et al. 2024), and climate monitoring (Camps- Valls
ct al. 2021 Reichstein et al. 2019). Despite their success in time series prediction (Mahmoud &
Mohammed 2021] Wang et al. (024), the black-box nature of deep neural networks (Zhao et al.
2023) and their in sensitive domains (Wang & Chung 2022) make explainability essential. When it comes to online time series, practitioners often place greater emphasis on prediction differences between adjacent time steps rather than isolated predictions, identical predictions can signify different contextual meanings. For instance, in clinical settings, a decrease in the probability of sepsis from 90% to 50% indicates patient improvement, while increase from 10% to 50% signals potentially severe deterioration (Boussina et al., [2024) Shashikumar et al., (2017). This epitomizes the necessity to contextualize prediction changes online time series and attribute them the features driving such transitions.
Albeit diverse explainable artificial intelligence (XAD) methods for time series (Ribeiro al. 2016
Lundberg & Lee 2017: Sundararajan et al., [017] Shrikumar et al.) 2017: Suresh et al. 2017
Tonekaboni et al., 2020 Leung et al. [2033 Crabbé Van Der Schaar 2021: Ennuehard]] 2023 Liu
et al. 2024bb Queen et al. [2244] Liu et al. 2024a]Jang et al. 2002) have been suggested, existing
approaches mostly explain predictions at isolated time steps, neglecting how predictions have evolved
over time. In this regard, directly applying single-time attribution methods to explain prediction
Equal contribution. "Equal advising.

---

Published as a conference paper at ICLR 2026
changes poses significant challeeg First, these methods inherently overlook the contextual dynamics of attributions. For instance, this frequently leads to practically irrelevant explanations (see Section for more details). Second, it is generally impossible to explain prediction differences using isolated attributions from individual time steps, which are irrelevant to prediction changes. For example, reveass that the existing XAI approach (Crabbé & Van Der Schaar 202T) produces implausible attributions when subtracting across two time steps. Last, appropriate evaluation metrics for attribution in explaining prediction changes remain largely unexplored. Common evaluation metrics, such as performance degradation after removing salient features, often exhibit limited correlation with attribution quality in evolving prediction settings.
To bridge this gap, we introduce Delta-XAI, a unified framework for explaining prediction changes in online time series monitoring. We begin with a problem setup of explaining prediction changes in online time series, attributing features to differences between adjacent steps rather than single-step predictions. Within this framework, we successfully adapt 14 mainstream XAI methods--iclludin including
general approaches (Ribeiro et al. 2016 Lundberg & Lee 2017 Sundararajan et al. 2017 Shriiummr
et al.
2017, 1017,,Suresh et 2017)) ) and time series-specifie ones Tonekaboni et al. |020; Leung et al.
2023
Crabbé & Van Der Schaar 2021 Enguehard 2023 Liu et al.) 2024b Queen et al. 2024 Liu
et al. 2024a] et al. 2025) via a wrapper function that transforms single-time explanations to
directly quantify prediction changes. We also propose a novel evaluation suite for online time series XAI, assessing attributions for faithfulness, sufficiency, completeness, coherence, and efficiency, providing a holistic standard over fragmented practices. Within this framework, we uncover that conventional gradient-based methods like Integrated Gradients (IG) (Sundararajan et al. 2017) typically outperform recent alternatives, consistent with recent findings single-time settings (Jang et al.. 2025).
Motivated by these observations, we introduce Shifted Window Integrated Gradients (SWING), a novel XAI method for explaining prediction changes in online time series. IG typically exploits integration along a straight path from zero baseline to the input, failing to capture temporal dynamics and inducing generalizing the integration path with the retrospective prediction window as baseline and performing piecewise line integrals through intermediate windows between two time steps. This yields faithful and interpretable attributions that capture temporal dynamics and mitigate OOD issues, while satisfying desirable theoretical properties for online time series monitoring completeness, implementation invariance, and akew-symmerry Extensive experiments under a wide range of settings, spanning diverse datasets and model architectures, verify that SWING outperforms existing baselines.
diitrii-iitiiitoonn (OOD) problem. SWING addresses these issues by
Our contribution can be summarized as follows:
We formulate a problem of explaining prediction changes in online time series monitoring and propose a unified framework that adapts 14 existing XAI methods via prediction wrapper, alongside comprehensive evaluation metrics tailored this setup.
We propose SWING, an XAI method for online time series that extends IG by incorporating past observations into the integration path, enabling temporal dynamics to be captured while mitigating OOD effects, while satisfying key theoretical properties.
Through extensive experiments across diverse benchmarks and backbone architectures, systematically analyze existing time series attribution methods and demonstrate that SWING surpasses state-of-the-ee alternatives under diverse evaluation metrics.
PROBLEM SETUP
Let f RWDD + Jo, 1] be online time series classifier where W is the fixed lookback window size, D is the number features, and C' is the number of output classes, which can be either binary or multiclass. Given online time series X E RRLD of length L, the model receives each time step T the input window - E RW D and outputs predicted class probabilities ([X--++17) E AC-1 {p E 1] DE Pe = 1}. In a conventional time series XAI setup, the goal is to explain the model's prediction at time T by estimating the contribution of each input feature within the window: for each t and d, existing approaches calculate the attribution (ff,Xx,, T), which quantifies the contribution of feature at time to the model output f fXr (XX--++1 w:7:) )a where ? = arg max, (XT- we
2

---

Published as a conference paper at ICLR 2026
(a) Input Features
(b) Model Predictions
(c) Attributions of Existing XAI
(d) Attributions of O'Ors
Sepsis Probubiltt (%6
Vital Sign
120
100
Body Teipp
Pulse
Tamp
Body
1.3
10.5
4.4
10
Temp
Body
40.1
0.1
-.4
80
Resp. Rate
60
Polse
3.7
Pulsc
3.1
Blooch Pressure
40
Resp.
4.1
9.7
2.9
Resp.
-04
0.1
-3.7
0
40
20
Prediction
Current Prediction
Blood
1.9
11.1
3.5
Blood Pressure
Rase
o.
0.7
-.6
Pressure
G
7
17g
(a) Input Features
(b) Model Predictions
(c) Attributions of Existing XAI
(d) Attributions of Ours
Vital Sign
100
80
Pulse
Body Temp
Scpsis Probablity
100
80
Temp
Body
1.8
2.7
14.2
10
Temp
Body
7.9
11.5
0.7
5
10
Reep, Rate
Puise
1.4
2.1
23.7
Pulse
9.6
833
1.3
60
40
Resp-
1.0
1.4
Resp.
20
Current Prodiction
Prodiction
Blood
Rate
2.2
2,0
19.6
-10
Bload
Rate
11.2
7.6
0.8
-5
31
T2
TT
T;
T
Pressure
Ti
25 7:
Pressure
T4
- 7:75
-10
Figure 1: Motivation for explaining prediction changes through illustrative scenarios that are not generated by actual XAI outputs. (top) Vital signs across Ti, T2. T3: risk rises from 10% to 90% then partially recovers (70%). Conventional attribution at T3 misleads, while our method highlights features driving recovery. (bottom) Risk evolves from 10% at Tj to 80% at T2 and slightly increases (85%) T3 due to delayed effects. Our method attributes prediction changes between Ti - T2 and T2 > T3. resolving these issues. Here, To denotes a hypothetical past time step at which the baseline input is placed to compute the initial score.
In contrast, our goal is to explain prediction changes between two time steps Tj < Tg. We define the target class as the one with the largest probability increase: é arg max, [(XT,- -XXT--W11T) )a. with the corresponding change A = (IXT:-W+1:77
[(XT,- ------1-,T Ja. To explain this change A, we compute attributions over t E [Ti W + +l...., T2]. d E {1..... D}. where each (ff Xt.d Ti + Th) quantifies the contribution of feature d at time to A. By default, we take è be the class with the largest probability increase. motivated by the fact that increasing- probability classes are often the most practically relevant. For instance, in disease risk prediction, even normal remains the top class, a sharp rise respiratory failure probability from T1 to T2 is clinically significant and merits explanation. However, Delta-XAI also supports flexible setups such as attributing the difference between the predicted classes, i.e., f(x,, - )a, where é arg max fXy; -Xx;:w+1-22 +1-2;;;)) )c and 0₂ arg max, f(Xr,- -1:1 )c- though this is not our primary concern. We further restrict to T. - Ti < W. as larger gaps yield non-overlapping windows and no common temporal context for attribution.
f(XT:- )as
3 MOTIVATION: CHALLENGES IN ONLINE TIME SERIES EXPLANATION
In time series applications, stakeholders often care less about static predictions and more about why they change. Standard XAI methods attribute importance to single prediction, but clinicians, financiers, or engineers instead want know which features caused risk to rise, fall, or remain stable. For instance, a doctor may ask which signals explain a patient's 's sudden recovery, or an analyst may seek factors driving credit score drop. Explaining such changes requires attribution methods tailored to highlight features responsible for prediction differences over time.
With regard this, existing XAI methods explain single-tim time predictions without historical context, making it hard to tell whether a feature caused risk to rise, fall, or stay stable. The top of Figure [ shows a sepsis model over three steps Ti < T2 < T3: risk rises from 10% at Ti to 90% at T2, then drops to 70% at T3 (b). Clinicians want explanations for the recovery between T2 and T3. but standard methods (c) still highlight T 's features, linking them to high risk. Explaining this 20% reduction instead requires attribution methods explicitly designed for prediction changes (d).
Real-world time series further suffer from irregular sampling and imputation, e.g., by forward filling. As shown in the bottom part of Figure I when all values at T3 are imputed (a), standard methods (c) wrongly assign high attribution to them because models emphasize recent inputs, whereas clinicians require attribution for actual observations. Our setup (d) instead supports time-wise attribution,
3

---

Published as a conference paper at ICLR 2026
computing T2's attributions from the change Ti - T2 and Ty's from T2 - T3, thereby highlighting genuine observations while de-emphasizing imputed ones.
DELTA-XAI: A FRAMEWORK FOR EXPLAINING PREDICTION CHANGES
In this section, we elaborate on our approach for attributing prediction changes online time series monitoring. To achieve this, we first introduce prediction difference wrapper that seamlessly adapts existing single-prediction time series explainers to explain prediction changes; we also highlight the special cases where the wrapper function simplifies to differences of attributions due linearity properties (Section 4.I. Second, motivated by empirical findings that classical Integrated Gradients (IG) outperform recent alternatives, specifically designed to explain prediction differences, incorporating historical contexts and thereby addressing the limitations of IG in explaining prediction changes for online time series (Section 4.2
propose Shifted Window Integrated Gradients (SWING),
4.1 FROM STATIC TO DYNAMIC: EXTENDING XAI TO PREDICTION CHANGES
Given an online time series classifier f RWxD + [0, Ijc, our goal is to attribute prediction change ( f(Xr; ---1----10 ----- with è arg max, - between two time steps Ti < T2. However, in general, it is impossible to explain prediction differences using isolated attributions from individual time steps, since they are irrelevant to such change. Indeed, neither computing attributions
f(Xz; -151;:W++30 -W+1 )c
differenced inputs (as f' is generally nonlinear), nor subtracting attributions across outputs (as attribution algorithms are also nonlinear), provides valid explanations, often yielding implausible results; for example, Table [ shows that Dynamask (Crabbé & Van Der Schaar 202T) produces misleading explanations when subtracting attributions across two time steps. To address this, we devise prediction difference wrapper g:
(g(TT-Ti+W>e + 1 9(XT, -w+1,))) ==
f(X,, -w+T))) (I)
which allows any single-prediction XAI method o be directly applied explain prediction changes:
(f, xt Ti - T2) = p(g. X, T2), Vt (Ti W + + 1..... 72),
(1...., , D}. (2)
This wrapper reformulates attribution of prediction differences as a single-prediction explanation, making broadly applicable across existing methods. It further enables single-time XAI approaches to be adapted with little or no modification, as detailed in Section
Special case: linear and complete XAI methods. While our wrapper function g is broadly applicable, it requires recomputing attributions for each time pair (Ti,T2) T2). When &p satisfies linearity in the attribution space, prediction changes reduce to the difference of single-time attributions, allowing those from one step to be reused across any pair that includes it, without computing them through g:
p(f ( Ti - T2) ((f x,, .d Te) (ff Ti).
If yp also satisfies completeness in the attribution space, e.g., SHAP variants (Lundberg & Lee 2017) IG (Sundararajan et al., (017), DeepLIFT (Shrikumar et al.) (017). this formulation guarantees online completeness, ensuring that summed attributions exactly match the prediction change:
Theorem 1 (Attribution Decomposition Theorem for Online Completeness). Given a linear and complete attribution method yp with fixed baseline, the following decomposition holds:
To Σ pel,,e.n T2)
[XTx- - ((X7,
[Ti+1d=1
Addition of newestt features
(4)
Te- W
Σ Σ lelf T2) x(f.X Ti)l
Σ
E (f Xea (T))
To W d=
d=
Delayed effect intermedlate features
Removaal of oldert feanures
This theorem indicates that summed attributions across all features and time points exactly equal the prediction change, providing clear interpretability. The proof is in Section F
4

---

Published as a conference paper at ICLR 2026
Window Size
9iG (f, Xt,5 (22)
W-in
PiG (f, x (T))
Vi.1
PId (,, Xia
Th)
Y2.1
PIG
feo
X, T2)
12-
T2
172
2
71.2
PSWING (f.
T + TT)
(Pid (e,
Th) 920 (e, T:))
mm
Figure Overview of the proposed SWING framework for explaining prediction changes in online patient monitoring. SWING extends conventional Integrated Gradients (IG) by replacing zero-baseline straight paths with line integrals over shifted historical windows and piecewise-linea paths, capturing temporal dynamics and mitigating cuttof-diisributtinn effects.
4.2 SWING: SHIFTED WINDOW INTEGRATED GRADIENTS
Standard Integrated Gradients (IG) remains competitive for explaining prediction changes but suffers from OOD artifacts and ignores temporal dynamics. We propose SWING, extending IG with retrospective baseline selection (RBS), ii) dual-path integration (DPI) ensuring online completeness. and iii) piecewise-lineat historical integration (PHI), yielding reliable explanations while preserving key theoretical properties. The overall pipeline is shown in Figure 2 with the detailed procedure outlined in Algorithm
Extending IG to line integrals over parameterized curves. We generalize IG as a line integral over parameterized curve y [o, 1] W connecting a baseline X' to the input X1-w+1.1
Pio Xe. T) @f(r(a)) a())( da.
(5)
0x.d da Here, standard IG appears as the special case of straight-line path 7(a) (1-a)X + WITT. where (a) = X yielding pia (f, Xt,a 7) = (Xt,d-Xi So 0f(x +a(X-X da.
2x.
Retrospective baseline selection. Motivated by the intuition that the most realistic baseline is the recent past observation, we generalize the baseline to the window steps before the input. For an input window XT Sr-w+1:11: this generalized baseline is -
set d = 1 as the default, since the immediate past provides the most stable and realistic reference. Formally, with d 1, we denote by 7 [0. the input 11:,, parameterized as: 7a(ax) = (1 ax) a)xTT - + -oXT:-W41:7 W+177, a E [0, 1]. This keeps (a())) near the data manifold and mitigates OOD issues, yielding:
In practice,
R RWDD the straight-line path from the baseline X7, XT.WWTT-1 to
PRBS X, Ti T2) pla (f. X Te) P7d (f. X,, T.).
(6)
Dual-path integration. Since PRBS uses distinct baselines at Tj and T2, relying single path may lead to incomplete explanations. Thus, we define ov. [0, 1] R2 Wxx as the straight-line path: (a) = (1 ax Nx; - + aXT, -XT,,W++:T,, four inseline-input pairs Fig and averages the results:
[0. 1]. i,j E 2]. DPI integrates along all
(f, Ti Tb)
Σ 1=1
PiG 3. 5. (f. T2) - 91G (f,X,, (i)).
St.1
(7)
This symmetric construction balances both baselines and, more importantly, ensures online complete- ness in Theorem [ that summed attributions equal the prediction change.
5

---

Published as a conference paper at ICLR 2026
Piecewise-linear historical integration. When the temporal gap between the baseline T; and the target at T is large, directly interpolating between X2.-W-I--1 and X2, -1,-2+1:2) may traverse regions far from the data manifold, leading unstable attributions. To prevent such off-manifold transitions, we introduce a piecewise-linear sliding path (a(a)) that incrementaly shifts the window across intermediate historical time points, ensuring smooth and temporally consistent progression from baseline to target. We describe the case i < j; the case > is obtained by reversing the temporal order. Let M T T; denote the number of window transitions between T; and T3. and for a E [0, 1] set:
K =
LaM
ā
aM K
(8)
seyment index
local interpolation ratio
Then, the integration path is defined (a) (11-a)X7;+K As a increases from 0 to 1, the interpolation shifts smoothly from the baseline window at T; the target window 7 in M small linear steps, ensuring that the trajectory remains close to the temporal data manifold rather than jumping directly across distant windows. This sliding construction yields more stable and faithful attributions for long-range temporal changes. SWING then aggregates contributions over all baseline baseline-target target combinations, producing a temporally consistent explanation while preserving online completeness:
+ -:+1::7 K-
PSWING Xt. Ti + T2)
Th.2 PGG
(f,
(f,Xe,d
T2) +
PiG T61
(f, Ti)).
(9)
2
In practice we uniformly sample a at Tisamples points within [0, 1] obtain tractable approximation of the path integral.
Theoretical properties of SWING. SWING extends the axiomatic guarantees of IG-Online Com- pleteness, Implementation Invariance, and seee-Symmetry providinvvi principled interpretations of online prediction changes. The proof's provided in Section
Theorem 2 (Online Completeness). The sum SWING autributions equals the prediction difference between two time steps: Ei, (SwING(sf (fanncc((,,,,, Xt,d Ti - T2) = ((Xx.-W+1rr)) - -(1T,
Theorem 3 (Implementation Invariance). SWING attributions depend only the model function, remaining invariant to equivalent network impllementations.
Theorem "skew-Symmetry) For reversed prediction changes, SWING attributions satisfy skew-
symmetry: (SsWING (f,X, Ti T2) -PswiNg (f. Xt, T2 TT))
EVALUATION METRICS FOR EXPLAINING PREDICTION CHANGES
This section introduces new metrics for online time series monitoring. We first examine issues with zero and average imputation (Section 5.1.. then propose metrics for attribution faithfulness and sufficiency (Section 5.2), and finally extend beyond these to a unified evaluation standard (Section 5.3
5.1 PROBLEM OF EXISTING EVALUATION METRICS
Modern time series XAI methods (Liu et al. 2024b a) typically assess futhhfuesitt how well attributions reflect model decisions and sufficiency- how well retained features preserve predictions- by substituting removed features with zero or average baselines.
Table Substitution analysis for XAI evaluation.
Substitution CPD
OOD Score
These substitutions ignore temporal locality and autocorrelation, yielding outtof-distribution (OOD) samples. Our analysis on MIMIC- III (Johnson et al. 2016) (LSTM backbone, SWING attributions) in Table shows that zero/average substitution produces substantial OOD samples, with high Cumulative Prediction Difference (CPD) and large OOD scores measured by conditional generative model MSE, exaggerating prediction differences and distorting correlations. We therefore adopt forward-filll -illing for faithfulness evaluation, reduces OOD effects and improves reliability.
28.12
Average
14.85
0.222
Forward-Fil
12.98
0.093

---

Published as a conference paper at ICLR 2026
5.2 PROPOSED EVALUATION METRICS FOR FAITHFULNESS AND SUFFICIENCY
Recently, TIMING (aag et al. 2025 has identified a key issue in faithfulness and sufficiency evaluation of time series XAI: removing top/bottom salient points simaltaneously can inflate scores by rewarding mere sign alignment. To mitigate this, TIMING introduced Cumulative Prediction Difference (CPD) and Cumulative Prediction Preservation (CPP), which remove features sequentially, In our wrapper setting with input X:
K-1
K- (x(x)) - 9 DIl CPP(g, x, K) = Σ A=
Co K) = Σ
g(x Dll (10)
where X and X denote the inputs obtained by removing the top-k and bottom-k features, respec- tively, from the full of (T2 - Ti + W) D features across the entire time window. These metries showed that gradient-based methods like IG (Sundararajan et al.) 2017) often outperform recent masking-basee ones (Liu et al. 2024b] Queen et [dl.])2]]] Liu et al. 2024a), a trend we also find for online prediction changes.
However, these metrics have key limitations: 1) they ignore the relative ranking among top-k features, 2) they overlook evolving attributions across time, and 3) they assess only ranking, not magnitude. To address 1), we introduce area-based metrics, Area Under Prediction Difference/Preservtior (AUPD, AUPP). AUPD is defined as the average of CPD values over all prefixes of the top-k features:
K
g X, K)
Σ k=1
CPD((,, X,) + CPD(g, A
1)) --1=1))
(11)
2K
AUPP is defined analogously using CPP instead of CPD. For 2), we aggregate attributions with
centered sliding window: (Xx,, d T) 1/(2W - 1) D W (X,,a T"). We denote the (MPD, MPP) and their area-based variants as Area Under Macro Prediction Difference/Preservatinn (AUMPD, AUMPP). For 3), we propose Corr., the correlation between ordered attributions and prediction differences:
resulting macro-level metrics as Macro Prediction Differencce/Preseraatie
(p, Corr. (10(), ([o(0,...,,pk)))
,(), ((K)
(WWDD 6 (WDD)
(12)
|oo 161l 19k
1
g6 g ........
1 - gk -
Together, these metrics capture ranking consistency, temporal dynamics, and attribution magnitudes, providing more faithful and interpretable evaluations.
5.3 BEYOND FAITHFULNESS AND SUFFICIENCY: BROADER EVALUATION METRICS
Existing time series XAI studies Tonekaboni et al. 2020 Leung et al. 2023 Crabbé & Van
Der Schaar 2021 Enguchard 2023 Liu et al. [024b) Queen al. 2024 Liu et al. 2024a) mainly assess faithfulness and sufficiency. While crucial and expanded here with nine detailed metrics- these alone are insufficient for practical utility. We therefore incorporate: 1) Coherence, checking alignment with domain knowledge (case study); and 2) Time/Memory Complexity, measuring real- time feasibility (empirically). Together, these provide a more comprehensive evaluation of XAI methods.
6 EXPERIMENTS
In this section, we comprehensively evaluate SWING against 14 time series XAI baselines within our Delta-XAl framework. We first describe the experimental setup Section 6.1 and subsequently address the following key research questions through our empirical analysis:
Does SWING provide more faithful explanations of prediction changes compared existing time series XAI baselines? (Section 6.2)
What is the contribution of each component of SWING to overall performance, as revealed
through ablation studies? (Section 6.3
7

---

Published as a conference paper at ICLR 2026
Table 2: Performance comparison of XAI methods on clinical prediction tasks: MIMIC-III decom- pensation benchmark using LSTM as backbone architecture. Evaluation is performed by removing the most or least salient 50 feature points per time step, using forward-fill substitution.
Algorithm
CPD
Removal of Most Salient S0 Points
AUPD
AUMPD
CPP
Removaa of Least Sallent 50 Points
AUPP 4
MPP
AUMPP
Con::
LIME RRheiiro et al. 2016
2.26
1.72
13.78
7,70
32.46.036
14,26.000
$3.45,mm:
15.30
1.02.
GrddSFAP 1 Limnter en & Lac 2017
13,73
9.05
16. 16.68
11.19
32.97.015
13.962006
50.135017
0. 0.14 11.95
Sundararaii &
9.10+eas
16.14
11.31
33.55=001
13.97:00
29.464017
10.85+a0s
+.07
DeaLIF1 Starkunar e
2017
13.58:2006
9.424004
16.032nes
11.25
35.96-t.16
14.61
31,53+015
+1.41:000
0. 4.000000
FD
Suresh Cell 2019
13.14
9.92+
17.79
12.92
44.02=c.e
22.32:tum
16.92
0. 0.26 6.24
AR
1020
13.24.poo
1.30
17.16100
11.95
36.13.cm
16.64
Lom
24.13
0.28
2.70
7.11
6.14
35.52:6.1
12.07:0as
10.19
WinfT
[1:3000 Bi 2125
19.64.pm
12:25.00t
24.87.am
15.45100
29.22_cmm
13.05.0m
26.11
11.9220
0.21 1000
Dynamaix (C12ON &
Der Schaar 2021
11,72400
7,564004
13.15an0s
8,25+m04
53.07=t34
26.22=000
49.80ssu1
24,26
0,04
Extmask
Enzochird
10.47
17.51sam2
10.63suus
29.91
15.13:0m
29.64aau17
14.84
Contral_S
2i 20740
12.88=2ro6
8.69
18.00
11.1]+01
11.62=t.0
21.17=609
42.67:422
21.91
0.03+t00
TimeX
(Quten or BC 20.4
16.99
11.45
19.45
12.45
50.34:c.0
24.11
51,061um
24.50:ate
0.03
TimeXer
11.12.005
7.001001
13.14;0m
7,761009
34.212417
13.72.0m
1.03.000
TIMING
Iang al
14.99.mm
9.71
16.50
11.531001
31.22_c.6
13.36_00
27.19
10.24
0.19
SWING
23,87:006
16:2340
22,27
15,52:au2
17.76=304
5,85=0
18,20
6.06
00440:0000
How does SWING perform qualitatively, and does produce temporally coherent explana- tions? (Section 6.4)
How does SWING behave under diverse evaluation settings, and what are its computational characteristies? (Section 6.5)
6.1 EXPERIMENTAL SETUP
Datasets.
Following prior work (Liu et al. 2024a b), two large-scale clinical datasets commonly adopted for online time series monitoring: MIMIC-III (Johnson et al.] 2016) for decom- pensation prediction and PhysioNet 2019 (Reyna al., 2020) for early sepsis detection, where predictions are updated as new data arrive. To assess generalizability. we also test on Activity, a human activity recognition dataset (Vidulin al. 2010). and synthetic benchmarks with controlled temporal dynamics (Delayed Spike (Leung et al.
2023 Switch-Feature (Tonekaboni et al. 2020
Further details are provided in Section
Model architectures. We mainly evaluate XAI methods with LSTM architectures, a fundamental choice for time series classification (Tonekaboni et al. 2020: Leung et al., 2023). To show our framework's versatility, we also implement CNN with stacked convolutions and Transformer encoder for long-range dependencies.
XAI baselines. We comprehensively implement and evaluate all XAI methods in Delta-XAL includ- ing our proposed SWING. Existing methods are categorized as: 1) modality-
perturbation-
based (LIME (Ribeiro et al. 2016). FO (Suresh al. (017), AFO (Tonekaboní et al.) 2020);; 2) gradient-based (IG (Sundararajan et al. 2017), DeepLIFT (Shrikumar et al. 2017), GradSHAP (Lund- berg & Lee 2017)): and 3) time series-specific methods, including online explainers (FIT (Tonekaboni
et al. 2020). WinIT (Leung al. (202)). masking frameworks (Dynamask (Crabbé & Van Der Schaar
2021 ExtrMask [Enguehard] 2023), ContraLSP (Liu et al. 2024b), TimeX (Queen et al. (024)),
TimeX
Liu et al. 2024a)), and TIMING (Jang al.
2025 augmenting IG with random masking.
Implementation details. Our method uses a single hyperparameter (Nsammlles = 50). We set T-2 T 1 for adjacent-step explanations, remove K 50 points, and absolutize directional attributions for fairness. Explanations are obtained through the wrapper g, which highlights features driving the Ti + T2 change. Metries are scaled by 10 except for correlations, memory, and time, Results are reported as standdad error over five runs, with best and second- best marked in bold and underline, More details are at https::/gittan
6.2 RESULTS ON ATTRIBUTION FAITHFULNESS AND SUFFICIENCY
Main results. Tables and o shows that, under the Delta-XAI protocol. SWING achieves the best performance on most metrics across both clinical datasets (MIMIC-II and PhysioNet 2019). Other gradient-based explainers such as IG, IIF, and TIMING also perform consistently well. In
8

---

Published as a conference paper at ICLR 2026
Table Ablation study of SWING examining retrospective baseline selection (RBS), -ual-path integration (DPI), and piecewise-linear historical integration (PHI) on the MIMIC- III decompensation
benchmark (Johnson et al. 2016), with LSTM (Hochreiter & Schmidhuber 1997) backbone and
interval 72- Ti 24. We vary the baseline distance d (default: 1) and remove the most or least salient 50 feature points per time step, using forward-fill substitution.
Removal of Most Salient 50 Points
Removal of Least Salient 50 Points
Algorithm
CPD
AUPD 1
MPD
AUMPD
CPP
ALPP
MPP
AUMPP
Corr. f
w/o RBS. PHI
45.82=020
26,85=0.3
40.85+02:
26.31
104.05=0044
48.43-019
77.94=030
29.56=au1
0.20=000
w/o RBS w/o DPI (a.))
40.25=022
24.39:001
46.71
29.10
79.70
31.299.007
82.59
33.33-011
0.16:000
55.01 0.8
32.39
53.09
33.08.aa1
71.01
26.12_.m0
76.26_.
29.29
0.19
13.
2011
48.9810.4
29.78.0.24
82.28.009
13
83.69
33.31
1,11
d== =33
42.00=033
26,00=025
47.49:2044
29.74::234
68.53=040
25.60-018
71.88-062
27.34=022
0.19:000
41398_.031
25.65
47.18.0045
29.44.10.3
72.31
27.88_022
74.62..m
29.11
0,18
d' 10
41.02
14.94_
46.89.10.1
29.23
76.55
29.96 16
78. 11 19:4
31.04gm 16
0,17
SWING
41.07 .022 26.46_.11
50.58.0074
12.29
60.29 I.14
21.60 JuB
64.43 IL19
23.87 a.
10c0
contrast, surrogate-driven methods like LIME, TimeX, and TimeX++ exhibit lower performance on preservation metrics, likely due to the data- and typerparameter-sensitiee of their surrogate models. These findings underscore SWING's dominance and refine the performance hierarchy of XAI techniques within Delta-XAI.
Diverse synthetic and real-world benchmarks. On the real-world Activity dataset (Table 7 SWING achieves the highest scores across all metrics. On the synthetic State and Switch-Feature benchmarks, it performs best or second- best on most metries, with competitive results elsewhere, underscoring its robustness across both practical and controlled scenarios.
Different backbone architectures. To assess generalizability across architectures, we re-evaluate the five strongest clinical baselines and SWING on the MIMIC-III dataset using CNN and Transformer backbones in Table [ SWING outperforms competing methods across most of the metrics; these findings confirm that SWING maintains robust faithfulness across diverse backbones within Delta- XAI.
Larger time differences. To assess robustness over longer time gaps, compare SWING with five strong baselines on MIMIC-III at 72- Ti = 6 and 24 (Table 9). with WinIT reported only for 6 due to generator limits. SWING achieves best near-best scores most metries, with particularly dominant gains on preservation metrics, though performance gaps narrow for longer intervals as CPD and AUPD converge across methods.
6.3 ABLATION STUDY
This subsection examines the contributions of SWING eeapoonents-retroppective baseline selection (RBS), piecewise- lineer historical integration (PHI), and dual-path integration (DPI). The meaning of each ablation configuration and its corresponding mathematical formulation minutely detailed in Section[H] As shown in Table 3 removing both RBS and PHI causes substantial degradation, while individually removing either module also reduces performance, highlighting their complementary roles. Without DPI, the model attains the best faithfulness scores (CPD, AUPD, MPD, AUMPD) but falls behind SWING in preservation metrics, suggesting that DPI primarily stabilizes preserva- tion. Overall, SWING sacrifices a small margin in -iifiitionndifeereneee metrics to achieve strong preservation and correlation performance. Varying the baseline offset d further shows that using the immediate past window (d = 1) yields the best trade-off, as both too short (d = 0) and longer offsets (d 2 3) degrade results. Finally, SWING maintains stable performance across varying ssamples (10 to 100), demonstrating robustness to hyperparameter changes (Figure 4).
6.4 QUALITATIVE ANALYSIS
Case study on feature attributions. Beyond quantitative evaluation, we qualitatively assess XAI methods within the Delta-XAL XAI framework. Figures t0 show input trajectories with attribution heatmaps for fifteen baselines at T, - Ti = 1. FO, TimeX, and TimeX++ tend to spread attributions broadly across the time axis, while Dynamask and TimeX++ align closely with input fluctuations,

---

Published as a conference paper at ICLR 2026
reflecting their strong benchmark scores. In contrast, SWING yields sharper, localized attributions that emphasize recent time steps most responsible for prediction changes.
Coherence analysis. We further assess whether SWING aligns with clinical knowledge by in- specting attributions on a representative MIMIC-III case Figure A sharp SBP drop at the last time step increases risk (a, b), consistent with evidence linking hypotensive episodes to decompen- sation TTkk et al. 2025). In contrast, an SpO2 rise lowers risk (c), reflecting the stabilizing effect of improved oxygenation (Semler et al. ) while steep fall in blood pH raises risk (d), in line with studies associating acidemia with poor outcomes (Henrique et al., 2023). To maximize visibility, further closely examine how the features at the last. time step influence the model's predictions in Figure 11 A sharp blood pressure drop at the last time step increases risk (a), consistent with evidence linking hypotensive episodes to decompensation (Toki et al. 2025). In contrast, an SpO2 rise lowers risk (b), while an SpO2 drop increases risk (c), reflecting the destabilizing effect of oxygen desaturation (Semler et al. 2022). A rise in GCS lowers risk (d), consistent with evidence linking improved consciousness to better outcomes |Marincowitz et al. 2018). These examples demonstrate that SWING produces attribution patterns consistent with established clinical findings.
6.5 FURTHER ANALYSIS
In-depth analysis under diverse settings. To further examine SWING's behavior, we perform subgroup analyses by splitting cases according whether predictions class labels change (Tables I0 and M., where consistently preserves explanatory advantages. We also test different temporal resolutions with 24- and 72-length windows (Table [2). confirming that SWING yields stable attributions and clear gains over baselines across both horizons. These results demonstrate that SWING provides reliable explanations under diverse prediction dynamics and temporal contexts, reinforcing its applicability to real-world clinical monitoring.
Efficiency analysis: runtime and memory. We assess SWING's efficiency along two axes-runtt runttmm and memory footprint. As shown in Figure 3. SWING attains the highest AUPD while requiring only 0.35s per sample, comparable
other gradient- pradient-based explainers (DeepLIFT 0.11s, GradSHAP 0. 0.1) For memory, it consumes 448 MB per sample, identical to IG (448 MB) and close GradSHAP and DeepLIFT (438 MB). These results demonstrate that SWING achieves state-of-the-att explanatory quality without incurring additional computational or memory costs.
CONCLUSION
In this paper, we have introduced the task explaining prediction changes online time series monitoring and proposed Delta-XAI, a unified framework that integrates 14 XAI methods with dedicated evaluation suite for temporal dynamics. Through extensive experiments, we demonstrated that, when adapted, classical gradient-based methods, such as Integrated Gradients (IG), remain strong baselines. Motivated by this, we developed SWING, an extension of IG that robustly captures temporal feature evolution. We believe our contributions significantly advance XAI for online time series by shifting the paradigm from static interpretations toward dynamic, context-sensitive explanations-an essential step toward trustworthy AI in time-critical domains.
ETHICS STATEMENT
This work develops explainable AI (XAI) methods for online time series monitoring in domains such as healthcare and finance. We use only publicly available open-source benchmark datasets (e.g., MIMIC-III, PhysioNet 2019, Activity), adhering to their usage protocols and ethical standards, and do not collect new human subject data. Our contributions are methodological, aiming to enhance the transparency and interpretability of time series models. Potential misuse may arise if explanations taken as direct clinical or financial advice; therefore, we emphasize that outputs should interpreted by domain experts.
10

---

Published as a conference paper at ICLR 2026
REPRODUCIBILITY STATEMENT
We provide an anonymized implementation of Delta-XAI and SWING at the anonymous reposi- tory link httptttpp/gggtiiii dataset preprocessing, model architectures, training protocols, hyperparameter settings, and evaluation metrics- are provided in Sections 6 and G while theoretical proofs and pre-proeessing steps for MIMIC-III, PhysioNet 2019, Activity, and synthetic benchmarks are elaborated in Section G In addition, several ablation studies and robustness analyses over multiple iterations further validate the stability of results. All procedures are publicly available and fully reproducible, enabling independent researchers to reproduce and verify our findings.
XAI All experimental details-inclludnng
ACKNOWLEDGEMENTS
This work was supported by AITRICS and by the Institute for Information Communications Technology Planning & Evaluation (ITPP) grant funded by the Korean government (MSIT) (No. 2019-0-00075, Artificial Intelligence Graduate School Program, KAIST).
REFERENCES
João Bento, Pedro Saleiro, André F Cruz, Mário AT Figueiredo, and Pedro Bizarro. Timeshap: Explaining recurrent models through sequence perturbations. In KDD, 2021.
Aaron Boussina, Supreeth P Shashikumar, Atul Malhotra, Robert L Owens, Robert El-Kareh, Christo- pher A Longhurst, Kimberly Quintero, Allison Donahue, Theodore C Chan, Shamim Nemati, et al. Impact of a deep learning sepsis prediction model on quality of care and survival. npj Digital Medicine, 2024.
Tom Brown, Benjamin Mann, Nick Ryder, Melanic Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. NeurIPS, 2020.
Gustau Camps-Valt Valls, Devis Tuia, Xiao Xiang Zhu, and Markus Reichstein. Deep Learning for The Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. John Wiley & Sons, 2021.
Molnar Christoph. Interpretable Machine Learning: A Guide for Making Black Box Models Explain- able. Leanpub, 2020.
Jonathan Crabbé and Mihacla Van Der Schaar. Explaining time series predictions with dynamic masks, In ICML, 2021.
Arun Das and Paul Rad. Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey, arXiv preprint urXiv:2006.11377 2020.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. ICLR, 2021.
Joseph Enguehard. Learning perturbations explain time series predictions. In ICML, 2023.
Andrew Frank. Uci machine learning repository. https://archive.csssuu www.:iiiiiei......dd... 2010.
John Cristian Borges Gamboa. Deep learning for time-series analysis. arXiv preprint 170101877 2017.
Kaiming He. Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016.
Lilian Rodrigues Henrique, Micaela Bianchini Souza, Riad Mahmoud El Kadri, Márcio Manozzo Bo- niatti, and Tatiana H Rech. Prognosis of critically ill patients with extreme acidosis: a retrospective study. Journal of Critical Care, 78: 154381, 2023.

---

Published as a conference paper at ICLR 2026
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural Computation, 1997.
Hyeongwon Jang. Changhun Kim, and Eunho Yang. Timing: Temporaliy--wware integrated gradients for time series explanation. In ICML, 2025.
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimic-iii, freely accessible critical care database. Scientific data, 2016.
Changhun Kim, Yechan Mun, Sangchul Hahn, and Eunho Yang. Deltashap: Explaining prediction evolutions in online patient monitoring with shapley values. ICML Workshop Actionable Interpretabiliry, 2025.
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, et al. Captum: A unified and generic model interpretabilityy library for pytorch. arXiv preprint arXiv:2009.07896 2020.
Kin Kwan Leung, Clayton Rooke, Jonathan Smith, Saba Zuberi, and Maksims Volkovs. Temporal dependencies in feature importance for time series predictions. ICLR, 2023.
Zichuan Liu. Tianchun Wang, Jimeng Shi, Xu Zheng, Zhuomin Chen, Lei Song, Wenqian Dong, Jayantha Obeysekera, Farhad Shirani, and Dongsheng Luo. Timex+:: Learning time-series explanations with information bottleneck. ICML., 2024a.
Zichuan Liu, Yingying Zhang, Tianchun Wang, Zefan Wang, Dongsheng Luo, Mengnan Du, Min Wu, Yi Wang, Chunlin Chen, Lunting Fan, et al. Explaining time series via contrastive and locally sparse perturbations. ICLR, 2024b.
Scott Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In NeurIPS, 2017.
Amal Mahmoud and Ammar Mohammed. A survey on deep learning for time-series forecasting. Machine Learning and Big Data Analyties Paradigms: Analysis, Applications and Challenges, 2021.
Carl Marincowitz, Fiona Lecky, William Townend, Agostino Fabbri, and Timothy Sheldon. The risk of deterioration in ges 13-15 patients with head injury. Emergency Medicine Journal, 2018.
Nicholas G Polson and Vadim O Sokolov. Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 2017.
Owen Queen, Tom Hartvigsen, Teddy Koker, Huan He, Theodoros Tsiligkaridis, and Marinka Zitnik. Encoding time-series explanations through self-supervised model behavior consistency. NeurIPS, 2024.
Markus Reichstein, Gustau Camps- Camps-Valls, Bjorn Stevens, Martin Jung. Joachim Denzler, Nuno Carvalhais, and F Prabhat. Deep learning and process understanding for data-driven earth system science. Nature, 2019.
Attila Reiss and Didier Stricker. Introducing a new benchmarked dataset for activity monitoring. In ISWC, 2012.
Matthew A Reyna, Christopher S Josef, Russell Jeter, Supreeth P Shashikumar, M Brandon Westover, Shamim Nemati, Gari D Clifford, and Ashish Sharma. Early prediction sepsis from clinical data: the physioner/computing in cardiology challenge 2019. Critical Care Medicine, 2020.
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. "why should trust you?": Explaining the
predictions of any classifier. In KDD, 2016.
Yulia Rubanova, Ricky TQ Chen, and David K Duvenaud. Latent ordinary differential equations for irregularyysammpled time series. NeurlPS, 2019.
Matthew W Semler, Jonathan D Casey, Bradley D Lloyd, Pamela G Hastings, Margaret A Hays, Joanna L Stollings, Kevin G Buell, John Brems, Edward Qian, Kevin Seitz, et al. Oxygen- saturation targets for critically ill adults receiving mechanical ventilation. NEJM, 2022.
12

---

Published as a conference paper at ICLR 2026
Omer Berat Sezer, Mehmet Ugur Gudelek, and Ahmet Murat Ozbayoglu. Financial time series forecasting with deep learning: A systematic literature review: 2005-2019, Applied Soft Computing, 2020.
Lloyd S Shapley, A value for n-person games. In Contributtions to the Theory of Games, Volume II. 1953.
Supreeth P Shashikumar, Matthew D Stanley, Ismail Sadiq, Qiao Li, Andre Holder, Gari D Clifford, and Shamim Nemati. Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics. Journal of Electrocardiology, 2017.
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. Learning important features through propagating activation differences. In ICML, 2017.
Mervyn Singer, Clifford S Deutschman, Christopher Warren Seymour, Manu Shankar-Hari, Djillali Annane, Michael Bauer, Rinaldo Bellomo, Gordon R Bernard, Jean-Daniel Chiche, Craig M Coopersmith, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA, 2016.
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. ICMI., 2017.
Harini Suresh, Nathan Hunt, Alistair Johnson, Leo Anthony Celi, Peter Szolovits, and Marzyeh Ghassemi. Clinical intervention prediction and understanding using deep networks. MLHC, 2017.
Takayuki Toki, Kazuyuki Mizunoya, Misa Itabashi, Naoki Nishikawa, Koji Hoshino, Hitoshi Saito, and Yuji Morimoto. Acute decompensated right heart failure potentially triggered by multiple factors including pulmonary vasodilator removal during plasma exchange: case report. JA Clinical Reports, 2025.
Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David K Duvenaud, and Anna Goldenberg. What went wrong and when? instance-win feature importance for time-series black-box models. NeurIPS, 2020.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention all you need. NeurIPS, 2017.
Vedrana Vidulin, Mitja Lustrek, Bostjan Kaluza, Rok Piltaver, and Jana Krivee, Localization Data for Person Activity. UCI Machine Learning Repository, 2010. DOI: http:///o..og//02243335776
Yue Wang and Sai Ho Chung. Artificial intelligence in safety-critical systems: systematic review. Industrial Management & Data Systems, 2022.
Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Yong Liu, Mingsheng Long, and Jianmin Wang. Deep time series models: A comprehensive survey and benchmark. arXiv preprint wrXiv:2407.13278, 2024.
Zeda Xu. John Liechty, Sebastian Benthall, Nicholas Skar SKar-Gislinge, and Christopher McComb. Garch-informed neural networks for volatility prediction in financial markets. In ICAIF, 2024.
Ziqi Zhao, Yucheng Shi, Shushan Wu, Fan Yang, Wenzhan Song, and Ninghao Liu, Interpretation of time-series deep models: A survey. arXiv preprint arXiv:2305. 2023.
Jianhu Zheng and Mingfang Huang. Traffic flow forecast through time series analysis based on deep learning. IEEE Access, 2020.
13

---

Published as a conference paper at ICLR 2026
A LIMITATIONS AND BROADER IMPACTS
Limitations. Our proposed approach has few limitations. First, while SWING extends IG by incorporating historical points with shifted window paths, it introduces additional computational overhead compared to simpler methods like standard IG. Second, while our prediction wrapper function can seamlessly incorporate most existing single-time time XAI algorithms, few require minor adjustments adjustments-particllattt part-cuulalllllyy those that rely on probability outputs or internal model representations. The specifics of such adaptations are tangential to our main contribution and are left to future algorithm designers.
Broader impacts. Our work significantly advances explainability in time-critical domains by en- abling a nuanced explanation of temporal prediction changes. By providing insights into why and how model predictions evolve, our framework supports decision-making in critical areas such as health- care, finance, and transportation, potentially improving outcomes through enhanced transparency and accountability. While responsible interpretation and privacy considerations remain important, the benefits of improved trustworthiness and actionable insights in high-stakes environments significantly outweigh these concerns.
B LLM USAGE DISCLOSURE
In drafting this manuscript, made limited use of large language models (LLMs) for minor writing improvements, such as grammar polishing and readability enhancement. The LLMs were not used for research conception, experimental design, analysis, or generation of substantive content. Its role was strictly limited to language editing, and all scientific contributions are attributable solely to the authors.
C RELATED WORK
-agnosiic explainable artificial intelligence. Although deep neural networks have achieved impressive results across domains like vision (He et al.) 2016: Dosovitskiy et al., (021)) language (Vaswani et al., 2017 [Brown et al. (020), and time series (Gamboa 2017)) they often act as black boxes, limiting transparency and accountabilityeespeiii
in high-stakes areas such as healthcare (Christoph) 2020). To address this, various modality-agnostio XAI methods have been developed. Popular approaches such LIME (Ribeiro et al., (016) and SHAP (Shapley [953 Lundberg & Lee 2017) attribute predictions to input features by estimating their contribution strength and direction. Variants like KernelSHAP, GradientSHAP, and DeepSHAP (Kokhlikyan al. 2020) expand their applicability. Gradient-basee methods, including Integrated Gradients (IG) (Sundarara) jan al. 2017) and DeepLIFT (Shrikumar al. 2017), compute attributions using model gradients. Perturbation-based methods like Feature Occlusion (FO) (Suresh et al. 2017) and Augmented Feature Occlusion (AFO) (Tonekaboni
al. 2020) measure feature importance by replacing inputs and observing prediction changes. While these methods have enhanced model explainability. most have been evaluated in vision tasks (Das Rad 2020). Their application time series peries-particullrrt for explaining prediction changes in online settings remiings-remain limited, despite the importance of capturing temporal dependencies for meaningful explanations.
Explainable artificial intelligence for time series. XAI for time series presents unique challenges due to temporal dependencies, where the order and historical context of observations significantly affect model behavior. Standard modality-agnostic XAI methods, which often assume independently distributed inputs, fail to capture such dynamics. To address this, a number of time series-specific
attribution methods have been proposed (Bento et al. 2021 Tonekaboni et al. 2020: Leung et al.
2023 Crabbé & Van Der Schaar 2021 Enguehard [023] Liu et al.) 2024b Queen et al. 2024 Liu
ct al. 2024a Kim et al. (2025). More recent methods have improved temporal modeling through dynamic masking (Crabbé & Van Der Schaar 2021 Enguehard] 2023), contrastive learning (Liu
et al. 2024b), and interpretable surrogate modeling (Queen et al.] 2024 Liu et al. 2024a). with TimeX incorporating an information bottleneck to mitigate trivial explanations. TIMING (Jang ct al. 2025) introduces novel evaluation metrics and enhances IG with random masking to improve
sensitivity to temporal variation. Despite recent progress, existing methods fall short in explaining
14

---

Published as a conference paper at ICLR 2026
prediction changes in online time series, lacking contextual insight and temporal evaluation. Our Delta-XAI addresses these gaps by attributing prediction changes directly and introducing metrics aligned with sequential dynamies.
Explainable artificial intelligence for online time series monitoring. Among time series XAI methods, FIT (Tonekaboni et al. 2020) and WinIT (Leung et al.) (023) are particularly relevant to online prediction tasks. FIT estimates feature importance by comparing predictive distributions under observed and counterfactual inputs using KI. divergence, whereas WinlT models delayed effects by assessing how past observations influence future predictions. However, our proposed framework significantly extends beyond these methods by offering a comprchensive and unified approach. Unlike FIT, which quantifies feature importance solely based on predictive distribution changes at consecutive time points, and WinIT, which evaluates feature relevance within fixed observation windows both producing static attributions across the entire series our Delta-XAL explicitly explains prediction changes between distinct time points. Specifically, we attribute changes in model predictions directly rather than attributing individual predictions independently, thus generating dynamic and setfiitionntim-sseettte attributions that accurately capture temporal evolution in feature importance. Additionally, we introduce SWING, an advanced attribution method demonstrating superior empirical performance and fulfilling essential theoretical properties, including linearity, completeness, and directional attribution. Finally, our framework systematically integrates existing attribution methods and proposes specialized evaluation metrics tailored explicitly to assessing prediction change explanations in online time series monitoring.
D ADAPTING EXISTING XAI ALGORIHTM
In this section, we provide detailed description of how adapt existing XAI baselines setting. Specifically, Section D presents the algorithms that can be applied without modification, i.e., with seamless integration into our prediction-diffrenct framework, while Section D.2 describes those that require minimal adjustments. In both cases, the adaptation is realized through a wrapper function g. which standardizes the attribution process for time series inputs. Notably, this wrapper neither necessitates additional post-traie eest-trainigg procedures nor alters the underlying models, ensuring fair and consistent comparison across baselines.
D.1 No MODIFICATION
The following attribution algorithms operate without modification: LIME (Ribeiro et al. 2016).
IG (Sundararajan et al. 2017). DeepLIFT (Shrikumar et al., 2017), FO (Suresh al. 2017).
AFO (Tonekaboni et al. 2020). WinIT (Leung et al. (023). Dynamask (Crabbé & Van Der Schaar 2021), Extrmask (Enguehard) 2023), Son (Liu al. 2024b), and TIMING (Jang et al. 2025).
In all of these cases, the only adjustment involves the use of the cedfettion-diffrencce wrapper instead of the single-time prediction model f. This wrapper applies uniformly across baselines
without altering their internal mechanisms, so the algorithms remain unmodified in our framework.
D.2 MINIMAL MODIFICATION
The following attribution algorithms require minimal modification: GradSHAP (Lundberg & Lee 2017), FIT (Tonekaboni et al. 2020), TimeX (Queen et al. 2024), and TimeX++ (Luu al. 2024a).
GradSHAP. Applying GradSHAP (Lundberg & Lee 2017) directly to the wrapper g violates completeness, since the baseline input for g depends on whether the evaluation is at Ti or T2. For example, Ti corresponds to a window - and T2 to X7;-W+1.Th, then the baseline for g differs depending which window is active, leading to inconsistency. To address this, compute GradSHAP directly on f. perform two runs with shared baseline, and subtract the resulting cuributioossmmirorii the construction of SWING. This procedure preserves completeness while remaining consistent with the prediction-differeee formulation.
FIT. For FIT Tooneaboon et al., 2020), we bypass the wrapper g and operate directly on the base model We construct both the current and previous input windows, Xr, - W:11 and X7, - W
15

---

Published as a conference paper at ICLR 2026
and compute the corresponding class- outputs f(X12-W+1:1; )a and f(Xn -i1--+++110 )e. where is defined as in the main text. At each time step and feature dimension, we sample candidate imputa- tions from the generator, apply them to XT,- w72-++1:T2- and measure their effect -( )a- We then quantify the divergence between the perturbed prediction and the original prediction dif- ference f f(Xr,-W+: --TT:T)) f(XT, -1-1-:++1// )a- For the divergence metric, follow the original implementation and support both the KL divergence and the mean absolute deviation. This adaptation preserves FIT's perturbation mechanism while aligning with attribution of prediction differences.
TimeX. For TimeX (Queen et al. 2024), the prediction- wrapper is implemented as Python class, where the forward () method returns the prediction difference f -(X,,-W-::T,) ((Xr,ww-:TT) ). To support the Model Behavior Consistency (MBC) loss, the class defines an auxiliary method that returns the difference between latent embeddings the two time points. Specifically, while TimeX formulates the MBC loss LMBC as:
LMBC Z, (2
Σ (22(, N
1
D2 2f,))
(13)
we obtain latent embeddings using the encoder of the model f with f = dec o enc. For an input sequence - we split into windows -T,-W+1:T, and ST.-w+1:P then compute embeddings as:
2 enc (115- -
(14)
We also experiment with concatenated embeddings 2 = [ene((T;- -11T,, -w+ )] and observe negligible differences in performance. In either case, this embedding construction provides a meaningful space over which the surrogate model operates, while remaining consistent with the predictionddiffrrnnce wrapper g.
TimeX++. For + (Liu et al. 2024a), we adopt the same strategy: emheddings are obtained in the same manner in TimeX, and the surrogate model operates the difference 27, 27 We further modify the label consistency loss. Instead of the cross- cross-entropy formulation in the original implementation, we employ mean squared error (MSE) loss for stability. Here, denotes the original input sequence and X denotes the perturbed version of the input produced by the explanation method. Concretely, the label consistency objective LLC is defined as:
(Lc(f()),f(
E (f) [X))]
(15)
following the Jensen-Shanmnn Shannon divergence form in (Queen et al. (024). but realized with an MSE surrogate. This yields loss function better aligned with the prediction-differet framework.
E ALGORITHM
We provide the detailed procedure of SWING in Algorithm which computes attribution scores for prediction changes in online time series monitoring. The algorithm explicitly defines integration paths using historically observed shifted windows, computes gradients along piecewise-linee paths via interpolation, and averages attributions from dual integration paths. This approach ensures compliance with the completeness property, provides realistic integration trajectories, and mitigates out-of-distribution (OOD) issues.
F PROOFS
F.1 PROOF OF THEOREM
By completeness of so, for any baseline X' E IR DD
we have:
((xm) f(X')==
Σ
Σ
@ffxXed [2),
(16)
t- W+1d=1
and similarly,
T
((XT.-W4IT))) -fX'))
Σ
Σ
pf, Ti).
IT).
(17)
t= W+1d=1
16

---

Published as a conference paper at ICLR 2026
Algorithm 1 SWING: Shifted Window Integrated Gradients
1: Input: Model f. inputs X7 - XT. w:7..7, discretization steps Neamples
2: Output: Auributions yoswING Xt,d
Ti + T2) for L E {Ti W 1.... T2), d
(I...., D).
3: for each Y5.J with i, {1 do
4:
f- [Tg - (T; 1)1,
a + sigu (Tj - (T; - 1))
( t Xr, -W:T-:
5:
6:
G(O)
)
7:
PIG
Th5 & O TT W
8:
for m m//nsappls nsamples
Psamples do
D Parallelized in our implementation
9:
Om
10:
K c- min (Lam MJ. M
ā f Qm-MA local interpolation ratio
segment index
11:
8 8 (T; - +)+
12:
X ( (1 - (1-
X + + +xX (axlaaaxx(t)))))))) (x())
13:
(mm) + (0( /
G(m)+ (m(m))
910 1id (t,d) d) 2hs (t,d) + (X(m) x, x(m-1))
14:
vt,
2
15:
end for
16:
M +- [T (Ti 1)1, t sign (T} - (T: (T;-1))
17:
end for
18:
2
PfwING(f, Xtd | + TT) + 2
Σ
FiG
21.2
(f,Xra
TT)
PiG 76.1
(f, Xe, (i))
i=1
By subtracting two equations, we obtain:
T2 Σ
f(XT, ((Xmm
Σ
pl Xt, (bb)
+1+11
(18)
T--W
Σ
Σ L -
TT)
Σ
Σ (ff [T). (T).
t= d=1
I= w+1d=1
F.2 PROOF OF THEOREM
Lemma 5 (Completeness of Integrated Gradients along General Paths). Let WXD -RC be continuously differentiable and let ? [0, 1] - IR W x be any continuously differentiable curve with ((0) X' (baseline) and (11 X1-W+1: (imput). Define the generalized Integrated Gradients attribution for each coordinate (t, d) by:
Pf T):= S'' Of(((a))) @na(a)) da.
(19)
ox.a
da
Then completeness holds along any such curve:
Lid T) ((X== f(X'
(20)
Proof. Stacking coordinates into a vector gives:
Eia (T) = f Vilrla))
V ds = ((r())a--(rrl)) (21)
by the Fundamental Theorem Line Integrals, since the integrand is the gradient field of the scalar potential fe. Substituting 7(1) Xy +1.7 and 7(0) X' completes the proof.
17

---

Published as a conference paper at ICLR 2026
Proof of Theorem
2
By Lemma 5 we have:
Σ
P1G (f, x, T2)
71.2
-
1= W +++1d d=1
T2
D
9IG
72.3
(f, x (2) = ((x1,-
W+1 d=l
(22)
PIG (f,X, [T) - ffannne--f(pppp - -w
Ti
T2
D
22.1 9IG
(S,
aa Ti)
1=11
d=1
SWING averages the attributions of the two paths to XT,- - and subtracts the average of the two paths to XT₃ - yielding:
E PSWING (f. Ti + T2)
1
Σ
Xe.d T2) PPG Xt, Ti))
fiG
(23)
2 ( -/ = -(x /(Xn-w+1nnbb
which establishes online completeness.
F.3 PROOF OF THEBRREM
SWING is defined average of Integrated Gradients (IG) attributions computed along multiple paths y. For continuously differentiable curve 7 [0, 1] + RWx2 DD with 7(0) X' (baseline) and y(1) X (input), the IG attribution is:
Xi, L.' 0Se(7(a)) Ona((a) da.
(24)
0x., da
Therefore, the resulting SWING attribution sPsWING (ʃ, at, obtained by averaging over its desig- nated paths, depends solely on the function S(X) and not on the particular architecture or parame- terization used to realize f. Consequently, any two models implementing the same function yield identical SWING attributions, establishing implementation invariance.
F.4
PROOF OF THEOREM
By definition,
Σ DfwwNGGf,X.. Ti T2)
1
74.2 PIG
(f, X,d [2) - Σ Pi (fxX,, Ti)). (25)
Consider the reversed prediction change T2 TT SWING uses the same four designated curves but traversed in reverse, so each IG term flips sign (antisymmetry of line integrals under reversed limits) and the two T22 terms and the two Ti terms swap roles. Hence,
Σ (f, Xr. T3 Ti) 1
1
Σ
Σ t
Tea (f.Xea (T)) + Σ PIG
TE2 (f, d T2))
PIG
2
1=1
Σ
PfWING(f. Xx.d Ti (i+T2)).
(26)
18

---

Published as a conference paper at ICLR 2026
Table 4: We evaluate our method on five datasets: three real-world Penchmarks-MMIMCCC JJonnson et al.
201 PhysioNet 2019 (Reyna et al. 2020). and Activity (Reiss & Stricker 2012) and two
widely y used synthetic datasets Delayed Spike (Leung et 2023) and Switch-Feature Tonekaboni
et al. 2020
Liu et al. 2024b).
Type
Name
Task
# ID
Sample ID
Window Size
Feature
Class
MIMIC-III PhysioNet 2019
Decompensation prediction
6,221
5
48
32
Real-world
Sepsis prediction
8,066
5
48
40
Activity
Human action recognition
200
50
12
7
Delayed Spike Switch-Feature
Binary classification
1,000
5
40
3
Symhetic
Binary classification
600
5
50
3
2
G DETAILS OF DATASETS
MIMIC-III For the MIMIC-III Clinical Database (Johnson et al. 2016), adopt the decompen- sation prediction benchmark defined in Johnson et al. (2016). The dataset consists of over 41,000 ICU stays from 2001-2012, with rich multivariate time series covering vital signs. laboratory values, interventions, and demographics. Following the benchmark setup, we use sliding windows of length 48 hours with prediction horizon of 24 hours, labeling an instance positive the patient dies within the horizon. This yields roughly 2.5 million prediction windows, of which about 63,000 (2.5%) are positive. Each window contains irregularly sampled trajectories across up to 32 variables, making a challenging setting for both temporal modeling and interpretability.
PhysioNet 2019. For the PhysioNet 2019 dataset (Reyna et al. 2020), adopt the sepsis prediction task defined under the Sepsis-3 criteria (Singer et al. (016)) The dataset comprises nearly 40,000 ICU stays collected across multiple hospital systems, with each stay providing multivariate physiological time series such as vitals, labs, and demographics. We define prediction windows using 48-hour observation length and 12-hour prediction horizon, labeling sample positive if sepsis onset occurs within the horizon. This yields around 1.1 million prediction instances, of which approximately 27,000 (2.5%) are positive. Compared MIMIC-III, this dataset presents higher variability in measurement density across hospitals, offering complementary benchmark for evaluating both predictive performance and explanation reliability.
Activity. We adopt the Activity dataset (Frank) 2010) and follow the preprocessing protocol from Latent ODEs (Rubanova et al.) 2019). The dataset comprises 25 sequences from five individuals, each having around 6,600 time points. We segment each sequence into overlapping windows of 50 time points using a stride of 1 (unlike the stride 25 used in the original Latent ODEs paper). Labels provided at each time point across 11 fine-grained actions; to reduce ambiguity, we merge them into seven coarse classes as in (Rubanova et al. (0019): walking, falling, lying, sitting, standing up, all fours, and sitting on the ground. For evaluation, we split by individual: the first three for training, the fourth for validation, and the fifth for testing.
Delayed Spike. We adopt the Delayed Spike dataset (Leung et al. 2023), a variant of the Spike benchmark originally introduced by Tonekaboni et al. (2020) and later extended by Leung al. (2023). The standard Spike dataset consists of three multivariate NARMA sequences with added linear trends and random spikes, where the label flips from 0 to immediately after spike appears in feature 0 and remains positive thereafter. the Delayed Spike version, however, the label transition is shifted by two time steps: it becomes exactly two steps after the spike in feature O. This modification forces explanation methods to correctly identify the causal spike event rather than simply aligning with the delayed label change.
Switch-Feature.
We generate the Switch-Featuur dataset following the design in FIT (Tonekaboni ct al.) (2200. Similar to the State dataset, it is constructed based on a three-state hidden Markov model with an initial distribution (1/3, 1/3, 1/3) and the following transition matrix:
00.55
0.02
0.03
0.02
0.95
0.03
0.03
0.02
0.95
19

---

Published as a conference paper at ICLR 2026
Each hidden state emits time series from Gaussian process with RBF kernel (y 0.2) and a fixed marginal variance of 0.1 for all features. The mean vectors the three states are given by μ4 [0.8, 0.5, 0.2), #2 [0, 1.0, OJ, and Ma (0.2, 0.8). Labels yitt are sampled from
Bernoulli distribution Bernoulliyy [4], where:
(1 exp( (Xx,,)))))
if 8t
prlt]
(1 cxp(- (X,,2)
if 8
(27)
(1 + exp(exxpxppp -x,,)) if 81 2.
and 9; denotes the latent state at time f. In our experiments, we generate sequences of length 100 and extract multiple online prediction samples from each sequence using fixed-length sliding window of size 50.
H DETAILS OF ABLATION STUDY
In this section, clarify the meaning of each ablation configuration and present its corresponding mathematical formulation. We independently verify the contribution of each SWING component by removing both RBS and PHI, 2) RBS alone, and 3) DPI (71,1, 72,2). The following variants correspond directly to the results in Table 3 and ensure that each ablation is rigorously defined and remains faithful to the original algorithm.
SWING w/o RBS, PHI. This variant removes both the retrospective baseline selection (RBS) and the sescewsse-lined historical integration (PHI), leaving only DPI active. Both integration paths start from the zero baseline, and the resulting attribution reduces to subtracting Integrated Gradients computed at T2 and Ti:
SSINNG
= P10 (J. Xt,a T2) (f, N Tı),
(28)
where each path is straight line from zero to the corresponding input window 7{aa) = -.XT,w++.77 This configuration is mathematically equivalent to applying IG independently at two time points and comparing their attributions.
SWING w/o RBS. This configuration removes only the baseline-selection component. The retro- spective baselines Xr-ww:T--- are replaced with the zero baseline, while all DPI mechanisms and the PHI interpolation remain unchanged. As a result, only the initial segment of each integration path is modified: the path originates at rather than at (r,,Ww:,,- but all subsequent interpolation segments and the dual-path structure are preserved. This ablation isolates the effect removing temporal baseline adaptation while retaining both historical integration and dual-path comparison.
SWING w/o DPI (71,1, 72.2). This variant removes the self-window DPI components 71,1 and 72.2, while keeping the cross-window paths 71,2 and 72,1 active. Since the cross wonddw paths are essential for attributing prediction changes between Ti and Tz. PHI remains applied to them, yielding the following attribution:
PSWING (wl DPD = PIG Xe. Tb) - 71.2
7221
-ic (f. Ti).
(29)
By eliminating only the self-window paths while retaining the cross-window integration, this ablation isolates the contribution of DPI to SWING's temporal attribution mechanism.
I ADDITIONAL EXPERIMENTS
This section presents additional experiments that complement the main findings. The content is organized into extended quantitative evaluations, backbone generalization, longer prediction intervals, ablation and robustness analyses, efficiency, qualitative case studies, and clinical coherence.
Extended benchmarks. Tables 6 and provide further results clinical datasets, synthetic benchmarks, and the Activity dataset. Across all settings, SWING achieves the best or second-best scores most metrics, reinforcing its robustness across both controlled and real-world scenarios.
20

---

Published as a conference paper at ICLR 2026
Table 5: Evaluation metries for prediction-change explanations on the MIMIC-III decompensation benchmark with an LSTM backbone. We follow the same setting as the main experiments, but evaluate by removing the most salient 50 feature points per time step with forward -ill substitution.
Algorithm
CPD f
AUPD
Curr. t
Dynamask w/ Naive Subtraction
0. 0.14=mm00
0. 0.1220.00
-0.192000
Dynamask Delta-XAl
11.72.1008
7.56100.
0.04
Table 6: Performance comparison of XAI methods clinical prediction tasks: PhysioNet 2019 sepsis benchmark using LSTM as backbone architecture. Evaluation is performed by removing the most or least salient 50 feature points per time step, using forward-fill substitution.
Removal of Most Salient 50 Points
Removal of Least Salient 50 Points
Algoritiu
CPD
AUPD
f AUMPD t
AUPP
MPP
AUMPP
Corr. T
LIME (Ribeiro al
2016
0.29
1883aam
1,02-u30
3.66
1.82
3.96
2.09
-0.08
GmdSRTPP (uunther Tee
2017
1.60
0393
2.52
1.50__000
3.48_000
1.64jom
3.75.mm
1,86
0.02.000
IG
Sumdararaa al 2017
2.68
1.54gam
3.001am
1,93_ux0
3.19
1.43.om
3.43.um
1.6
n:00
Deat (Striooma
2017
2.72an00
160g
2.87.nm
1,86_n3e
3.06
1.35gom
3.38
1,56.n0e
0.16
ar
1.17
0.78
2.75
1.80_.x0
4.63_u03
3.13.cm
2.44
1000
AFO
Lonekaboons
2020
187=um
1.16
2.88+am1
1.86-030
3.12:1030
1.48=amm
3.28
1.55
0.15
FIT
Tnaekahoni
ju7e
0,48
1.101am
0,91__00
_,00
1.96.0m
1.30
1,02
0.00
Winlt [Leung et al 2024
1.86um 1.10zam
2.85
1,66-ux0
3.88-130
2.02
3.12.um:
1.64:1000
0,06=100
Dynamiik
(hinnn 2 Vin Der Schnar 1021
1.69 1,1
2.05.a0
1,36__n0
4.98_n3
2.77.om
4,74jmm
2.59_n00
0.06
[inguchart
1.161m
0.72
1.911m
1.11-0x0
4.04-u36
3.98:um
2.36:1400
Sontral
[Lu et a
202-16
0.73+am2
0.34=am
2.47=a12
1.32-03
5.32-0054
2.93=5m4
5.35
2.97=0044
0.04:000
TimeX
Queen
0.74
0,36
1.961am
1,00_.00
4.33
2.77
4.49
2.89
m
T
el
30244
1.764um:
1.66a00)
2.07:a001
1.19-u30
3,99-051
1.81
4.02+a0)
1,82:1000
0,05=000
TIMING
biigg er
2025
1,56
3.02
1.94 0..0
3.12_
1.41jom
3.38 jitto
ji30
1,60
0.13_no0
SWING
3.10:am
1,96:a00 2.81
1,78-03)
2.27-00
0.93:a/0 2.38
1.01:0m
0.32:602
Backbone architectures. Table 8 compares CNN and Transformer backbones MIMIC-III. The results show that SWING retains strong performance across both architectures, demonstrating versatility beyond LSTMs.
Longer time intervals. Table evaluates explanations at T2 - Ti = 6 and 24. Although CPD and AUPD converge across methods with longer intervals, SWING consistently achieves best or near-best results, and shows particularly dominant gains preservation metrics.
Ablation and robustness. Table 3alee3aane Janalzess the contributions of RBS, PHI, and DPI. Removing RBS
PHI degrades performance, confirming their complementary roles, while DPI mainly stabilizes preservation. Baseline offset analysis further shows that
yields the best trade-off, with d = 0 and larger offsets leading to degradation. Figure 4 confirms robustness across broad range of ssmpplss Subgroup and temporal resolution analyses (Tablees to 12p additionally show that SWING preserves its advantages regardless of prediction label changes and remains stable across both 24- and 72-length windows.
Efficiency. Figure demonstrates that SWING achieves state-of-thh-ant explanatory quality with- out significant computational overhead. Runtime (0.35s/sample) and memory (448 MB) remain comparable to gradient-base based basclines such as IG and GradSHAP.
Qualitative and clinical coherence. Figures 5 visualize attribution maps, where SWING produces sharper, localized explanations than surrogate or masking methods. Finally, Figure IO confirms coherence with medical knowledge, correctly highlighting risk increases from SBP drops and pH declines, and protective effects from SpO2 rises.
Overall, these supplementary experiments confirm that SWING consistently delivers reliable, efficient, and clinically meaningful explanations across diverse datasets, models, and experimental conditions.
21

---

Published as a conference paper at ICLR 2026
Table 7: Performance of XAI methods on (top) Activity (Vidulin et al., 2010). (middle) Delayed Spike (Leung et al. 2023), and (bottom) Switch-Feature (Tonekaboni et al. (020) benchmarks with an LSTM backbone. Evaluation is performed by removing the most or least salient 50 feature points per time step, using forward-fill fill substitution.
Algorithm
CPD
Removal Most
AUPD
Salient MPD
AUMPD
CPP 4
Removal of Salient Points
AUPP I
MPP
AUMPP
Com:
KF Sunderwajan al 2017
19.80a
12.534001
4.76
7.38
20.23
8.77
0.09
Desgial Doirl w/ 20 Min 2017
21.0
13.25
5.10as:
15.26a54
6.22
19.07
8. 0.20
0.17asus
AFO Tendeiion
15,20am
10.11
7.5
4.79
13:0
5666235
18.23
7.99
0.77
Winlf
6.62
3.34anuz
6.61894
3.87::
20.10a:23
20.16:1
9.45
TIMING
13.75eumm
7.18
4.35
19.30s108
9.28=333
19,4
8.69
SWING
21.77
14.96 oua
133
6.19
1.64
2370
1012
0.46 8.00
KF Sundaraaian a 2017
290.74gto1
246
7m
281 63414
225
13.14
4.30g347
15.78
5.59an1o
0.46-820
Deegua Mirrh u// 20 2017
269 1
251
277 38
228.97
22.97
11.47at23
3.69:225
1485
5.11
0.58
AFO Trmes thon 2010
256, 70
189.2
240
165
29
7:8
2.66::204
12.29
4,46an19
19:
Winlf
426.69:55 114.50
191 191886113
56,91a3
298405
344.09
180.94a10
46.32sm
-0.02ame
TIMING
Dang
327 199.72e220
205 82533
96.00a3
30a242
230.69:2.
205.20
92.31
SWING
317.95
252.161134
305.09:344
131.50-142
335
1.13:
4.70
1,85
0:44:800
KG Sundararajn et al
201
464
264.2
422.12a553
474.36a535
395.55-439
10.97at133
4.29
17,94.aa
6.561n16
0.65:000
Decii Strr lil of Me 2013
525. 16gtsi
470.902661
523.05-4
430 1i
6,6600
1445334
16.35
5.97
063
AFO Tead Fimm & mr 2010
533.944775
475 155.554677
529.43:677
431 48 18:
938463
308
13,30:a0
i.17
0.58
Winli
387.8
447 507.50
496 19
391 3
12:46a5.1
7.46:231
311.7laszs
113.50
0.53:m16
TIMING mnngerii
418.65
391.51+31
7.54
507.7las1
255.11
0.6480
536.43:6
183
519.55
436.18 40
7.32:
1.9
15.84
5.81
0.74:
Table 8: Performance comparison of XAI methods on MIMIC-III decompensation prediction task with different backbone architectures: CNN (top) and Transformer (bottom) architectures. Evaluation is performed by removing the most or least salient 50 feature points per time step, using forward-fill substitution.
Removal Must
Salient 50 Points
Removal of Leust Sulient 50 Points
Algoritiun
CPD
AUPD f
MPD1 1
AUMPD1 f
CPP
ALPP |
MPP J
AUMPP
Cors,
IG (Sundararaian et al. DedpLant
2017
28. 38.6884044
32. 16.83=0m
20 20.25=00w
11.37ea0
19.08
37.25=206
15.6349044
0.24
2017
29.59
17.60ate
12.14
20 20.
41.69:012
19.13
37.68:310
15.65
1.28
APO Tone Farnnr
21.641044
13.42.00
35
20.16200
50.97a
25:251007
34.301:31
14.4910
0.301900
WinlT Leung dt mi 2023
23.15
13.35
32,69
18.44
25.09 55.611016
42211pin
19.931mm
1.23
TIMING
29,804086
17.262006
32,33 12
20.45200
40,54:0 10
18 86-805
36,
10:300
15, 16.26800
0,265500
SWING
44.02:0.7
27.85 1.12 40.36:012
24.47:am
8.23:
:4.97
s.44
r.53
Algorithm
CPD
Removal of Most Salient 50 Points
AUPD
MPD1
AUMPD
CPP
Removal of Least Sulient 50 Points
AUPP
MPP J
AUMPP
Conr. f
KG SSundaranjjan et al 2017
16.33
9.77
15,261309
10.422005
33.522921
14.58a012
27.42=310
10.423000
0.152000
DeepLIFT uneer 4 di 2017
15.1
9.87=to
15.10sa00
10.53=cus
33,24
14.13
28.24=2.7
10.40eaa0
0,14
AFO
14.6119m
10:22:tu0
1735
12006
37.261019
18.10
23.78:336
9.23
0,23
Winlt
(Tguss nt.
17.20g005
1004
23.99
12.1ee
34.44g0 12
16.04100
25.922310
12,3618 06
0.122000
TIMING pang a 2125
22.53
12.59
15.9
T0.94 Zone
31.53,011
14.04
25.781906
10.01
0,18
SWING
25,50
16.38-ue
23,71
15:84=119
17.57=41
5:98=00
18.05
6.30
0,26:5000
Table 9: Performance comparison of XAI methods MIMIC-III decompensation prediction task with various time interval settings between two time steps Ti < Ty: 6 timestamps interval (top) and 24 timestamps interval (bottom) using LSTM as backbone architecture. Evaluation is performed by removing the most or least salient 50 feature points per time step, using forward-fill substitution.
Algorithm
CPD 1
Removal of Most Salient 50 Points
AUPD
MPD1
AUMPD
CPP 1
Removal of Least Salient Points
AUPP 1
MPP 1
AUMPP L
Corr.
IG (Sundararajon ot al_ 2017
44.17
45.53 26.92.mu
29.23
91.67_0w
39.68.017
76.671a2
28.60.c1
Deeptarr
2017
42,00:n1x
25.
44,50
28.542912
100.64ans:
41.62am2
84.92a44
30.38
0.19
APO (Tonekarver ar
35.21.00
12.98
47.47
300110
106.361497
49.11a77
65855a
25.13
0:22
Winli (Lenng er 20.5
$2.98 euc
18.
19.19.0
10.47100
91.22 om
43.191m01
83.181891
41.25.03
0.14
TIMING Jang
52,76
30.71
47.30
30. 30.3250
87.90
38.52.004
72.488335
27 15
0:
SWING
51.46:024
33.5
55.64:a3
35.81:024
61.00
20.75:011
63.95
21.90=
Algorithm
CPD
Removal of Most Salient 50 Points
AUPD
MPD1
AUMPD
CPP
Removal of Least Salient 50 Points
AUPP
MPP
AUMPP
Corr. f
(Snndararajon et 2017
45.75-mm
26.81
40.79
26.372a15
183
48.12
77.632244
29.38
0.20
DeepraFr (anniime ara 2017
40.55.018
24N01a0
40.
40.16
25.96 25.961
109.50
50.500
49.1110m
83.81287
30.47.011
APO TTegdaatoon 2010
31,04-621
20.46-n0
42.44
26.92sa15
120,00aac0
61.17a022
64.36:435
25.25-000
0. 186000
TIMING Marrannge ot
58.83.0m
32.381022
42.52
27.42an
97.93.04
46.752013
72.362233
27
0.22:48
SWING
41.07=0m
26.46suus
50.58
32.29:a21
60.29:014
21.60:0as
64.43
23.87=410
0,21
22

---

Published as a conference paper at ICLR 2026
Table 10: Subgroup analysis of attribution methods under different score-change conditions (high:
| 2 0.1 (top), low: | < 0.1 (bottom), where A - [(XT,- - - f(Xr, 1:T;)) )). Results are reported on the MIMIC-III decompensation benchmark with an LSTM backbone with T2- T2-Ti = 1. Evaluation is performed by removing the most or least salient 50 feature points per time step, using forward- fill substitution.
Algoritha
CPD
Removal Must Salient Points MPD
AUPD
AUMPD
CPP
Removal Least Salient 50 Points
AUPP
MPP L
AUMPP
Corr. 1
KG Sundaramjjn al. 2017
281 1003
222.011051
319.09
251.67aocs
381.59
186.83.03
147.10 249
1.07
De (sarikume Ji 2017
296.81 a13r
236.89a020
257.72 4295
226.361
371,5
181.49
334,79.as4
140,56aa44
0. 0.29
AFO Toncktivnn 2017
279.7 g601
224. 13
257 S3500
190.70
316.06-411
135.02
0.42
Wialf
2r23
141.14
14
408,03.0z
285.
a50.
182.05
366.12-134
194.42
1230000
TIMING ping
224.724043
253.72
378.7
184.84
346
34.25
145.34
SWING
311.94:033
4884
308.561475
96.11-aa2 26
264.34-477
98.10-92
0.57
Algorithm
Removal Most Salient 50 Points
Removal of Least Solient 50 Points
Corr. 1
CPD
AUPD
MPD ~
AUMPD1
CPP
ALPP
MPP L.
AUMPP
IG Sunlammninn el all 2017
11.Masu
7:44smnn
14.0fasse
9.53em
30.33
12.58
26. 26.29
9.56
0.09ame
Desp
11,42200
7.63.0e
13.79
32.05
13.29
28.57=337
0. 10.76
15.03aauur
10.20smue
33.20
15.29..044
21.31
8.24
0.19
Winli
10.82suu
22.35
13.65
25.97
11.39-a22
23.13
10,38-m
0 0.20eur
TIMING (aan cral 2023
13,04.00o
8.08
14.35
9.72
28.14
12.01 a03
24.18
9:05
0.11
SWING
14.09 Lear
1943360
13.341610
15.34
4.97 a.3
15.09 412
5.21 ax2
0.32
Table 11: Subgroup analysis of attribution methods with respect prediction stability. Cases divided into changed (top) vs. unchanged (bottom), depending on whether the predicted class label (i.e., arg max f(XT, -1--- vs. arg max f (XT=-W++:T)) )) is different or remains the same. Results are reported on the MIMIC-III decompensation benchmark with an LSTM backbone (I2- - 7i = 1). Evaluation is performed by removing the most or least salient 50 feature points per time step, using forward-fil fill substitution.
Algorithan
CPD
Removal Must Salient 50 Points MPD
AUPD
AUMPD
CPP
Removal Least Salient 50 Points
AUPP
MPP L
AUMPP
Corr. 1
KG (Sundammjjn et al. 7
227.09a021
177.97
256.60.o51
202. 19
277.70
138.81
107,53 252.27
0.4
D (Surikume
2017
230 a222
181.474620
256 18
202 44224
22288
136,23.04
246.01 am
104, 104,30
0 1.32
AFO (Tankkrrrr
216.8
171 28.28
260
203 285.22
276.04g1
145
145.05g1m
98 98.48
0.5
Winti
207.99103
340
238005000
130.44:09
(6155
0.372000
TIMING (lnn di
23000
179.521633
258.60
203.50104
276,34,433
137886
249.23.443
186.
1.44
SWING
323355
253
311 35
246.80
185
185,96:944
69.09
189.32 1.5
70.43 3.34
0.62:sue
Algorithm
CPD
Removal of Most Salient 50 Points
AUPD
MPD
AUMPP
CPP
Removal I Least Solient 50 Points
ALPP
MPP 1
AUMPP
Corr. t
1G Sundaranija
2017
12.39atue
8.26stue
15.01 stue
10.36aue
31,93aa31
13.25.a12
27.87
10.14
0.13s
Deeprarr Istrioin & 30
8.60se
10.32sme
34.14a
13.79-a12
29.75=12
0.16uu 10.70
12.361008
8.54
11.031006
34.50a030
15:86-a11
22.49
8.68
0.240000
Winli
Logs du 2012
18.591911
11.47aue
23.41:0a2
14.40
27,50aaus
12.20-2225
24.66
11.15=a0
0 0.201000
TIMING Jang alal 2013
14,7
8.92stue
15.37
10.582mu
29.36
12,58-a12
25.55
9:57=am
0.15auu
SWING
22.54:635
1505
20.94601
14.38
16,44:a35
5.39
17,02:431
5.65:a4
0.36:100
Table 12: Performance of XAI methods on the MIMIC-III dataset with an LSTM backbone, shown for window size 24 (top) and window size 72 (bottom). We evaluate by removing the most or least salient 50 points with forward-fill substitution.
Removal of Most Salient 50 Points
Removal of Least Sulient 50 Points
Algorittun
CPD
AUPD 1
MPD1 1
AUMPI>
CPP
ALPP
MPP J
AUMPP
Corr. f
IG (Sundararaian et a 2017
11,98s24:
7.84
13.91
9.40-2M4
24.08
10.69
20.67ssie
8.35
0.20
Dedprrer Strreiima a mir 2017
12.401247
8.31
13.93
9.45.211
25.3317m
11.05122
22.361145
8.901252
0.24
APO
20200
17.261332
11.612334
32.11
15.485300
22.382535
9.29s2.7
0.265000
Winl (Taing di nr.
16.25
10.36
22.21
13.44:344
27.0814
12.701500
25.44g500
12.241345
0.22
TIMING Pang af al. 2028
15.354307
8.43
14,22e3.5
9.59-215
22 22.42es
10.21+2
19.27e55
7.91+2
0.23
SWING
19.33
12.89
17.961435
12:22:338
13.20
4.37 1.35
14.21
1.53
0.49:mm
Algorithm
CPD
Removal Most Sallent 50 Points
AUPD f
MPD1 1
AUMPD
CPP
Removal of Lenst Sulient 50 Points
AUPP 1
MPP J
AUMPP
Corr. f
Sundarargaan
2017
8.71
5.93-504
10.234n35
7.16-4.0
15.642000
7:27=000
12.40=307
4,7620/2
0,236000
DedpLiPT Cumar 4 ai 2017
9.28
6.39-cuc
10.35
7.29-au
16.37sm
7.46-005
13.05
4.9
0.26
APO TTne Zonnnr er ai 2020
8.73100s
6.05_cm
10.861091
7.56. cm
16.111010
8.09
10.57
0.29 4.17
Winlf (Tuing
7.22-006
14.88:a4s
9.17-am
5.77-00
11.562300
5,532009
0.25a000
TIMING ping
9.14
10.361989
7:24_am
1473290
7:002mm
11.55
0.24100
SWING
13.79
9.46=
13:05
9.10=am
7,51:am
2.45:0a2
7.75
2.56:
0.48:0.9
23

---

Published as a conference paper at ICLR 2026
20
20
FO (1.66, 9.92)
Area Under Prediction Difference
Deep (0.11, 9.42)
Area Under Prediction Difference
SWING (448, 16.23)
15-
GraSHHAP (0.18, 9.05)
SWING (0, 35, 16.
15
GrddSHAP 9.05)
DeepLIPT
WinIT (0.68, 12.25)
TimeX (449, 12.25)
TimeX (0.01, 11.45)
TimeX
Extmask (65.33, 10.47)
Extronak (442, 10.47)
TIMING (0.10, 9.71)
(392,
10
10
(448, 9.71)
IG (0.08, 9.10)
APO (392, 9.30)
KG (448, 9.10)
Contral (3.83, 8.69)
Dynamask (1.90, 2.70)
Dymamask (438, 7.56)
TimeX-+ (0.01, 7,00)
TimeX-+ 7.00)
5
5
APO (1,78, 9.30)
So (440, 8.6
LIME (374, 1.72)
(522, 2.70)
LIME (L31, L.72)
0
2
6
350
500 Peak Memory (MB/sample)
400
450
550
Elapsed Real Time (sec/sample)
(a) Time complexity vs. performance
(b) Memory complexity vs. performance
Figure 3: Computational efficiency analysis comparing SWING with baselines the MIMIC-III
benchmark. (a) Elapsed real time per sample (sec/sample, log-scale) versus AUPD (K 50). (b) GPU peak memory consumption per sample (MB/sample) versus AUPD (K 50).
17
16
Area Under Prediction Difference
15
14
SWING
13-
IG
12
11
10
10
20
30
50
100
Resmpleee
Figure 4: Hyperparameter sensitivity of SWING with respect to nsamples compared to IG (dotted gray line).
24

---

Published as a conference paper at ICLR 2026
Table 13: List of clinical features used from the MIMIC-III dataset, including their indices and descriptive names for model input.
Index
Name
Index
Name
Index
Name
Index
Name
Height
11
Weight
22
GCS-Motor4
33
GCS-Total9
Hours
12
Blood pH
23
GCS-Motor5
34
GCS-Total10
2
Diastolic BP
13
Cap. Refill
24
GCS- Totalo
35
GCS-Totall
FiO,
14
GCS-Eye0
25
GCS-Totall
36
GCS-Total12
Glucose
15
GCS-Eyel
26
Total2
VerbaVerbalO
Heart Rate
16
GCS-Eye2
27
GCS-Total5
38
Verball
6
Mean BP
17
GCS-Eye3
28
GCS-Total4
39
GCS-Verba12
SpO2
18
GCS-MotorD
29
GCS-Total5
40
3
Respiratory Rate
GCS-Motorl
30
GCS-Total6
41
GCS- Ver-Verbal4
9
Systolic BP
20
GCS-Motor2
31
GCS-Total7
10
Body Temperature
21
GCS-Motor3
32
GCS-Total8
Previous Score: 0.04 Current Score: 0.04 (Score Diff: -0.01)
TWING
Figure 5: Qualitative case study showing attributions extracted with XAI methods on MIMIC- III (Johnson et al. 2016) using LSTM (Hochreiter & Schmidhuberi 1997) backbone with T; = 47 and 48, Le., T, T2 1. The uppermost- heatmap displays the normalized input features. while the remaining fifteen panels illustrate the attribution heatmaps generated by each XAI method under the Delta-XAI framework, reflecting their respective explanations of score changes.
25

---

Published as a conference paper at ICLR 2026
Previous Score: 0.04
Current Score: 0.03 (Score Diff: -0.01)
10
Figure 6: Qualitative case study showing attributions extracted with XAI methods on MIMIC- III (Johnson al. 2016) using LSTM (Hochreiter & Schmidhuber 1997) backbone with Ti 47 and 7, 48, Le., 7 = 1. The uppermost-left heatmap displays the normalized input features, while the remaining fifteen panels illustrate the attribution heatmaps generated by each XAI method under the Delta-XAl framework, reflecting their respective explanations of the score changes.
26

---

Published as a conference paper at ICLR 2026
Previous Score: 0.03
Current Score: 0.03 (Score Diff: 0.00)
Teryd
m
Dynmak
SWING
Figure 7: Qualitative case study showing attributions extracted with XAI methods on MIMIC- III (Johnson al. 2016) using LSTM (Hochreiter & Schmidhuber 1997) backbone with Ti 47 and 7, = 48, ie., 7 = 1. The uppermost-left heatmap displays the normalized input features, while the remaining fifteen panels illustrate the attribution heatmaps generated by each XAI method under the Delta-XAl framework, reflecting their respective explanations of the score changes.
27

---

Published as a conference paper at ICLR 2026
Previous Score: 0.03 + Current Score: 0.04 (Score Diff: 0.00)
IBM
APO
Figure 8: Qualitative case study showing attributions extracted with XAI methods on MIMIC- III (Johnson al. 2016) using LSTM (Hochreiter & Schmidhuber 1997) backbone with Ti 47 and T- = 48, ie., T 1. The uppermost-lefi heatmap displays the normalized input features, while the remaining fifteen panels illustrate the attribution heatmaps generated by each XAI method under the Delta-XAI framework, reflecting their respective explanations of the score changes.
28

---

Published as a conference paper at ICLR 2026
Previous Score: 0.03 + Current Score: 0.03 (Score Diff: 0.00)
IBI
Dopp.77
A00
Figure 9: Qualitative case study showing attributions extracted with XAI methods on MIMIC- III (Johnson al. 2016) using LSTM (Hochreiter & Schmidhuber 1997) backbone with Ti 47 and T- = 48, ie., - 1. The uppermost-lefi heatmap displays the normalized input features, while the remaining fifteen panels illustrate the attribution heatmaps generated by each XAI method under the Delta-XAI framework, reflecting their respective explanations of the score changes.
29

---

Published as a conference paper at ICLR 2026
Presinus Scat; Soore (Sovre 0.10)
Scuec: Cumaaii Saure (Soorc 0.26)
SKANI
Provious Scrve: 0,36 Camani Sair: 0.81 (Sorrc DiTl 40,55
Provisms Scuee: 0.45 - Cumani Sture 0,52 (Seare DiIl 0.07)
SACTNO
Figure 10: Qualitative case study for coherence analysis on decompensation risk. Fach subfigure (a-d) shows two heatmaps for MIMIC-III (Johnson et (0166) sample processed with LSTM (Hochre iter & Schmidhuber 1997) backbone: the left heatmap visualizes normalized input features, and the right heatmap displays SWING's feature attributions. These panels reveal the clinically relevant features and temporal patterns that SWING identifies as most influential for the observed changes in decompensation risk score.
30

---

Published as a conference paper at ICLR 2026
Probability Evolution: 0.227 - 0.549 (Difference +0,322)
Current Probabiity: 0.549
Systolic Blood Pressure (238.000 55.0000
Dinstolic Blood Pressure (173.000 30.000)
+0.148
Mean Blond Pressure (86.000 44.000)
SpO2 (79.2 29.000 94,000)
0.028
Heart Rate (129.000 19.00000--0 - 103.000)
Delayed Effeet Internediate Feature
00 005
Removal of Oldest Feature
+0,000
0.2
0.5
0.6
Previous Probability; 0.227
Probability Evolution: 0.285 0.574 (Difference +0.289)
Current Probability: 0.574
Sp02 (97,800 82.100)
-0.240
Resgiratory Rale (20.000 4.000)
0..49
Heart Rate (1 (116.000 #7.000)
.0.035
Delayed Effect Intermediate Feature
-0.035
Removal of Oldest Feature
+0.000
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
Previous Probubility: 0.285
31

---

Published as a conference paper at ICLR 2026
Probability Evolution: 0.248 - 0.118 (Difference -0.130)
Current Pmhhbility:: 0.118
Sp02 ($2.500 97.300)
-0. 116
Systolic Blood Pressure (125,000 122.000)
Mean Blood Pressure (86. 000 86.000)
Diastolic Blond Pressure (66.000 68.000)
Delayed Effect of Intermediate Feature
+0 0.004
Removal Oldest Feature
+0 +0,000
0.10
0.15
0.20
0.25
0.30
Previous Probability: 0.248
Probability Evolution: 0.526 + 0.388 (Difference -0.138)
Current Probability: 0.388
GCS. Verhalr (0.000 1.000)
-0.042
VCr-VVebalo (1,000 0.000)
-0,032
GCS-Motor5 (0.000 1.000)
-0.027
Delayed Effect Intermediate Feature
-0.037
Removal of Oldest Feature-
+0.000
0.30
0.40
0.42
0.46
0.50
0.52
0.54
Previous Probubility: 0.526
Figure 11: Qualitative case study for coherence analysis on decompensation risk. Each subfigure indicates a bar plot for a MIMIC-III (Johnson et al., (2016) sample processed with LSTM (Hochreiter) Schmidhuber 1997) backbone. Bars individual features represent the attribution contributed by newly observed inputs the current time step, while the 'Delayed Effect of Intermediate Features' and 'Removal of Oldest Features' summarize the aggregated attributions of their respective feature groups. These panels provide an intuitive understanding of how each variable contributes to the observed change in dccompensation risk score and illustrate the clinical plausibility of SWING's explanations.
32


Reviews:
[{"review_id": "yAa6LSeIZk", "reviewer": "Reviewer_z3oK", "summary": "This paper focuses on explainable AI (XAI) for improving the interpretability of model predictions in online time series monitoring. It reformulates the explainability problem into a new XAI setting that is more practically relevant for deployed online time series models. In addition to introducing a new perspective on explainability for online time series monitoring, the paper also provides a new set of metrics designed to better evaluate the performance of different XAI methods. Beyond proposing this new problem definition and evaluation framework, the authors adapt existing XAI methods using a wrapper under the new setup. Through extensive benchmarking and comparisons, the authors further propose a new XAI method called SWING, adapted from the Integrated Gradients (IG) approach, which achieves state-of-the-art performance compared to prior methods.", "questions": "1. Could the authors provide a more detailed explanation on why IG works better than others instead of just claiming it does empirically?\n2. The paper introduces a new evaluation suite (faithfulness, sufficiency, completeness, coherence, efficiency). However, these are still indirect proxies for human interpretability. Could the authors provide evidence or discussion on how well these quantitative metrics align with human-perceived explanation quality in real-world decision-making, especially in clinical contexts?\n3. For SWING, is there a systematic trade-off between the length of the temporal window, computational efficiency, and the interpretability of the resulting explanations?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The writing of this paper is clear and succinct. Figure 1 clearly motivates why time series monitoring requires a new framework for explaining feature attributions.  \n2. Instead of following complex or recent prior works, the authors modify existing classical IG methods to achieve state-of-the-art performance, demonstrating a strong understanding of the relevant literature.  \n3. This paper presents a comprehensive study that includes a new problem framing, a new set of evaluation metrics, a new method that performs well, and extensive experiments demonstrating the applicability of the proposed method and framework.", "weaknesses": "1. The SWING framework is an extension of the IG baseline. The choice of using IG as the base method for SWING is motivated by its strong empirical performance in prior XAI metrics. While this is a reasonable motivation, there should be a more qualitative or theoretical explanation of why IG performs so well compared to more recent methods.  \n2. It would be valuable to conduct a user study to assess whether practitioners who regularly interact with time series dashboards prefer this new type of explanation.  \n3. Since this work defines a new problem framework for XAI in time series, along with the data, data processing, and evaluation implementation, it should be released as a benchmark for future methods to build upon and test against.  \n4. The reviewer disagrees with how the data features are used for MIMIC-III. Splitting GCS scores into different features would likely increase rather than reduce noise in an already highly incomplete dataset.", "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": "6wKH94eYFM", "reviewer": "Reviewer_hFWL", "summary": "The paper proposes Delta-XAI, a wrapper that reframes explaining prediction change between two times as a single-prediction explanation, enabling adaptation of 14 XAI methods for online time series. It also introduces a tailored evaluation suite and presents SWING, an IG-based method that uses retrospective baselines, dual path symmetry, and piecewise historical integration to reduce path OOD and capture temporal dependence, achieving stronger faithfulness and preservation than IG at comparable cost, with code released.", "questions": "As mentioned in the weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe wrapper reduces the new task (change explanations) to standard single-step explanations, enabling broad reuse of existing methods.\n2.\tThe evaluation suite addresses multiple metrics (faithfulness, sufficiency, completeness, coherence, efficiency) tailored to evolving predictions.\n3.\tSWING is well-motivated for online streams and retains IG’s desirable axioms.", "weaknesses": "1.\tThe method is defined for prediction change, but experiments do not clearly stratify or stress-test stable segments where the delta is small. In online monitoring, long stable stretches are common.\n2.\tIn the problem setting, it is mentioned that it can solve multiclass classification, but it is doubtful since the target class is assigned with the one with the largest probability increase.\n3.\tThe current setup seems focused on overlapping windows (short gaps). The applicability to non-overlapping/larger gaps is not demonstrated. \n4.\tIt mentioned that this method can be applied to 14 existing XAI methods, but only show the IG version.", "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": "f5nMYiKMxd", "reviewer": "Reviewer_y8Zs", "summary": "The paper introduces SWING, a time-series explainability method that extends Integrated Gradients to a temporal setting. The novelty of this work is in providing explanations for changes in the model’s prediction rather than the prediction at a single time point, which is a realistic scenario in many applications. The authors compare SWING to existing time-series explainability methods by adapting those methods to this change-in-time setup using a wrapper function.", "questions": "1) In the feature-attribution case study, you state that SWING yields sharper, localized attributions emphasizing recent time steps most responsible for prediction changes. Why is this desirable?\n\n2) How does the wrapper handle positive vs. negative attributions? Do the scores need to be normalized to produce realistic explanations?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) The proposed explanation setup that focusing on changes in model prediction over time rather than the absolute prediction is compelling and well aligned with application domains, especially healthcare, where decisions often hinge on changes.\n\n2) The evaluation presented in the paper is very thorough: many baselines, multiple backbones, and more importantly analyses from several angles, including performance and computational cost.", "weaknesses": "1) The paper focuses on attributing prediction changes when the argmax class does not change from time $T_1$ to $T_2$. But scenarios where the class does change seem highly relevant. Does the method extend to class-change cases, and if so, how?\n\n2) The choice of retrospective baseline (using previous time steps) is intuitive for isolating the single transition’s contribution, but it can fail depending on the granularity of change. It may be better to allow path selection based on the specific output change to explain. For example, if a clinician observes a heart-rate increase from a few hours earlier, the baseline should be the window producing the prediction for the prior hour.\n\n3) Some metrics (CPP, CPD, AUPD, and Correlation) may not align with the paper’s setup. They measure sensitivity of the prediction to removing observations, not which observations most influence the change in prediction over time. These metrics seem appropriate for all earlier baselines that measure importance of features for prediction at a certain point in time, however, they don't necessary guarantee improved performance in the proposed dynamic setup.\n\n4) Please clarify the motivation for the dual path integration (DPI). I'm not convinced why this component is a necessary part especially given that In the ablation (top-k removal), DPI shows no benefit. Also, it appears to be a notation issue in the equation for this section: for $i=1$, the text integrates over $\\gamma_{1,2}$ for $T_2$ and $\\gamma_{1,1}$ for $T_1$. Did the authors mean $\\gamma_{2,1}$ instead of  $\\gamma_{1,1}$ ?", "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": "L9swiqw3iC", "reviewer": "Reviewer_QpEH", "summary": "The paper introduces SWING, an integrated gradients methods designed for time series for time series explainability. This explainability method mainly focus on why the prediction changes between 2 time points, instead of only explaining prediction at 1 time point. The paper also includes new evaluation metrics to suit more specifically to time series.", "questions": "- The equations should be numbered.\n\n- When I was reading the paper, I did not find any hint about Delta-XAI being a \"unified framework\" for the 14 existing XAI methods. To me, as a reader, the main idea of the paper is to introduce the explanation of prediction change from $T_1$ to $T_2$, rather than a single time points. Then for each time point, the paper tackles the problem of OOD for IG and specifically tackle the problem of having 2 time points $T_1$ and $T_2$. The notation for this \"framework\", $\\varphi(f, X_{t,c}|T_1\\rightarrow T_2 )$, is just to explain the model $f$ on data $X$ from time $T_1$ to $T_2$. This is a generic time series explainability statement. Thus I think saying that there is a unified framework introduced is misleading. Also, there is no discussion on why this is a unified framework anywhere in the main text of the paper. Thus I think the idea of it being a unified framework can be removed from the main contributions. There could be a small paragraph on the adaption of this notations to other method after the experiment section, if the authors really want this to be a framework.\n\n- The \"generic wrapper function\" $g$ is not really generic as in line 178, $g$ is defined to be the difference of the predictions (with a moving window).\n\n- I think the main idea of this paper is very neat. We tend to explain a prediction at a specific time point $T$ instead of the prediction change from $T_1$ to $T_2$. As the authors discussed this in section 4 about how merely subtracting the difference of the other methods (Dynamask) can produce misleading explanations, the authors did not do the same in the experiments in Figure 1. I think comparing columns (c) to columns (d) may not be the most fair comparison as the explanations in (c) is to explain a single time point. The author can tackle this by either do the \"difference\" comparison, or make the point of the usefulness of explaining the prediction changes is more useful than explaining a single-timepoint prediction.\n\n- The author should include the notation $\\varphi (f, X_{t, d} | T_1\\rightarrow T_2)$ and $\\varphi (f, X_{t, d} | T)$ in the first paragraph in section 2. This is to highlight the main notation being used throughout the paper.\n\n- (Minor) I believe the $\\gamma_{2, 1}$ is wrong in Figure 2. Shouldn't it be from $T_2 - 1$ to $T_1$?\n\n- For the RBS and DPI, I think there is one main concern about the rationale of this choice (both notations and also the formulae). From an IG perspective, when we want to use IG on an input X w.r.t. the baseline X', the IG explanations is to explain the *difference* between X' and X. Thus, in Line 235, if the baseline X' is set to be $X_{T-W: T-1}$ then this explanation should \"really\" be $\\varphi(f, X_{t,d} | T - 1 \\rightarrow T)$, instead of $\\varphi(f, X_{t,d} | T)$. So in line 245, the RBS explanations are actually the difference of explanations between the \"changes\" in $T_2$ (from $T_2 - 1$) and the \"changes\" in $T_1$ (from $T_1 - 1$). It is thus the \"change\" of \"changes\". For DPI, it makes more sense because the \"changes\" and calculated between 2 pairs of points. But the rationale of this selection is not explained. (This is shown in Figure 2)\n\n- Equations in line 261 are not the friendliest to the readers. The idea is quite simple. Maybe do this. Let $\\gamma_{T}^\\pm(\\alpha) = (1-\\alpha)X_{T-W+1:T} + \\alpha X_{T-W+1\\pm 1: T\\pm 1}$ be the line segment between two neighbouring sliding windows. Let $\\gamma_{i, j}(\\alpha)$ be the concatenation of the line segments $\\gamma_{T_m}^\\pm$ for $m=i, \\ldots j$ (or $m=j \\ldots i$).\n\n- Line 323, aggregating attributions with a sliding window - does this apply to everything? i.e. Top K features, correlations, other attribution methods?\n\n- In 6.3 Ablation study, there are descriptions of \"SWING components\" (RBS, DPI, PHI). The SWING attributions are described in line 267, which seems to include \"concepts\" of RBS, DPI and PHI. But it is unclear that we can remove each of them. So what does the formula look like for SWING without RBS, PHI, without RBS and without DPI separately?\n\n- In 6.3 Ablation, what is that baseline distance $d$? $d$ was used as the feature index in the above text (section 4) and it is not really defined here. If I am guessing, SWING corresponds to d=1 which corresponds to the description in the RBS (baseline set to the time point right before T). So I suppose that all the \"1\" will be replaced by \"d\" in equations 251 and 267? If this is the case, either removing these ablations from the table, or introduce this design in Section 4. (and resolving the notations for feature index and this baseline distance)", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "- The paper addresses many different issues of in the time series explainability paradigm and proposes a new method SWING to for time series explanations. This includes explaining \"prediction changes\" rather than a single \"prediction\".\n- The paper also include newer evaluation metrics in which I think they make sense. \n- The logic and flow of the paper is quite coherent in general.\n- Extensive results are included and the the results seem to show that SWING achieve the best performance.", "weaknesses": "- Several notations and presentations can be better in Section 4.\n- The \"Unified Framework\" introduced in the contributions in the introduction is almost nowhere to be found. It could be implied in the Appendix.\n- While the method (SWING) is to explain the prediction changes from $T_1$ to $T_2$, the other baseline methods aimed to explain the prediction for $T$. This can imply that the experiments are not completely fair (both to SWING and to the other baseline methods).", "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)