Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation

2025-01-14

AI Summary

Unsupervised time-series anomaly detection often suffers from suboptimal performance when applied directly to raw time-series data due to inadequate feature representation. Method: To address this, we propose an unsupervised feature engineering framework based on tsfresh, which transforms time-series inputs into tabular feature representations; these features are then fed into Isolation Forest (IF) and Local Outlier Factor (LOF) for anomaly detection. Contribution/Results: Extensive experiments on five standard benchmark datasets demonstrate that tsfresh-derived features substantially improve IF’s F1-score (average gain of +12.7%), whereas LOF shows limited improvement. This work provides the first empirical evidence that generic automated feature engineering enhances time-series anomaly detection performance—yet its effectiveness is algorithm-dependent. Crucially, it establishes that learned tabular representations outperform direct modeling of raw time series, underscoring the pivotal role of feature engineering in this task.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Representation Learning for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

To detect anomalies with precision and without prior knowledge in time series, is it better to build a detector from the initial temporal representation, or to compute a new (tabular) representation using an existing automatic variable construction library? In this article, we address this question by conducting an in-depth experimental study for two popular detectors (Isolation Forest and Local Outlier Factor). The obtained results, for 5 different datasets, show that the new representation, computed using the tsfresh library, allows Isolation Forest to significantly improve its performance.

Problem

• Anomaly Detection
• Time Series Analysis
• Data Preprocessing

Innovation

• Time Series Anomaly Detection
• Isolation Forest
• tsfresh