ICML 2025

TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
Hyeongwon Jang * 1 Changhun Kim * 1 2 Eunho Yang 1 2
Abstract
Recent explainable artificial intelligence (XAI)
methods for time series primarily estimate point-
wise attribution magnitudes, while overlooking
the directional impact on predictions, leading
to suboptimal identification of significant points.
Our analysis shows that conventional Integrated
Gradients (IG) effectively capture critical points
with both positive and negative impacts on predic-
tions. However, current evaluation metrics fail
to assess this capability, as they inadvertently
cancel out opposing feature contributions. To ad-
dress this limitation, we propose novel evalua-
tion metrics—Cumulative Prediction Difference
(CPD) and Cumulative Prediction Preservation
(CPP)—to systematically assess whether attribu-
tion methods accurately identify significant pos-
itive and negative points in time series XAI. Un-
der these metrics, conventional IG outperforms
recent counterparts. However, directly applying
IG to time series data may lead to suboptimal
outcomes, as generated paths ignore temporal re-
lationships and introduce out-of-distribution sam-
ples. To overcome these challenges, we introduce
TIMING, which enhances IG by incorporating
temporal awareness while maintaining its theoreti-
cal properties. Extensive experiments on synthetic
and real-world time series benchmarks demon-
strate that TIMING outperforms existing time
series XAI baselines. Our code is available at
https://github.com/drumpt/TIMING.
1. Introduction
Time series data are prevalent across various fields, espe-
cially in safety-critical domains such as healthcare (Sukkar
et al., 2012; Bica et al., 2020; Rangapuram et al., 2021),
energy (Rangapuram et al., 2018; Benidis et al., 2022),
*Equal contribution
1Kim Jaechul Graduate School of AI,
KAIST, Daejeon, South Korea 2AITRICS, Seoul, South Korea.
Correspondence to: Eunho Yang <eunhoy@kaist.ac.kr>.
Proceeding...