ICML 2025

When Will It Fail?: Anomaly to Prompt
for Forecasting Future Anomalies in Time Series
Min-Yeong Park * 1 Won-Jeong Lee * 2 Seong Tae Kim 1 Gyeong-Moon Park 2
Abstract
Recently, forecasting future abnormal events has
emerged as an important scenario to tackle real-
world necessities. However, the solution of pre-
dicting specific future time points when anoma-
lies will occur, known as Anomaly Prediction
(AP), remains under-explored. Existing meth-
ods dealing with time series data fail in AP, fo-
cusing only on immediate anomalies or failing
to provide precise predictions for future anoma-
lies. To address the AP task, we propose a novel
framework called Anomaly to Prompt (A2P),
comprised of Anomaly-Aware Forecasting (AAF)
and Synthetic Anomaly Prompting (SAP). To en-
able the forecasting model to forecast abnormal
time points, we adopt a strategy to learn the re-
lationships of anomalies. For the robust detec-
tion of anomalies, our proposed SAP introduces
a learnable Anomaly Prompt Pool (APP) that
simulates diverse anomaly patterns using signal-
adaptive prompt. Comprehensive experiments on
multiple real-world datasets demonstrate the su-
periority of A2P over state-of-the-art methods,
showcasing its ability to predict future anoma-
lies. Our implementation code is available at
https://github.com/KU-VGI/AP.
1. Introduction
As deep learning methods have evolved rapidly, their appli-
cation to time series analysis has gained significant attention
due to their critical importance in real-world scenarios. As
part of this, recently, forecasting future abnormal events
has been newly proposed in time series analysis, such as in
*Equal contribution
1Department of Artificial Intelligence,
Kyung Hee University, Yongin, Republic of Korea 2Department
of Artificial Intelligence, Korea University, Seoul, Republic of
Korea. Correspondence to: Seong Tae Kim <st.kim@khu.ac.kr>,
Gyeong-Moon Park <gm-park@korea.ac.kr>.
Proceedings of the 42 nd International Conference on Machine
Lea...