Long-Term Outlier Prediction Through Outlier Score Modeling

2026-03-21

AI Summary

This work addresses the challenge that traditional time series anomaly detection methods struggle to effectively forecast anomalies far into the future. To this end, it introduces long-term anomaly prediction as a distinct task for the first time and proposes an unsupervised, model-agnostic two-stage framework. The first stage performs standard anomaly detection to produce anomaly scores, while the second stage models the temporal dynamics of these scores to predict the likelihood of future anomalies. By integrating unsupervised learning, time series modeling, and temporal analysis of anomaly scores, the proposed approach demonstrates strong performance in both anomaly detection and long-term forecasting on synthetic datasets, establishing a robust baseline for future research in this emerging area.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

This study addresses an important gap in time series outlier detection by proposing a novel problem setting: long-term outlier prediction. Conventional methods primarily focus on immediate detection by identifying deviations from normal patterns. As a result, their applicability is limited when forecasting outlier events far into the future. To overcome this limitation, we propose a simple and unsupervised two-layer method that is independent of specific models. The first layer performs standard outlier detection, and the second layer predicts future outlier scores based on the temporal structure of previously observed outliers. This framework enables not only pointwise detection but also long-term forecasting of outlier likelihoods. Experiments on synthetic datasets show that the proposed method performs well in both detection and prediction tasks. These findings suggest that the method can serve as a strong baseline for future work in outlier detection and forecasting.

Problem

• long-term outlier prediction
• time series
• outlier detection
• outlier forecasting

Innovation

• long-term outlier prediction
• outlier score modeling
• unsupervised anomaly detection
• time series forecasting
• two-layer framework