TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly
  Detection with Inexact Supervision
Chen Liu Shibo He Haoyu Liu Shizhong Li
Time series anomaly detection (TSAD) plays a vital role in various domains
such as healthcare, networks, and industry. Considering labels are crucial for
detection but difficult to obtain, we turn to TSAD with inexact supervision:
only series-level labels are provided during the training phase, while
point-level anomalies are predicted during the testing phase. Previous works
follow a traditional multi-instance learning (MIL) approach, which focuses on
encouraging high anomaly scores at individual time steps. However, time series
anomalies are not only limited to individual point anomalies, they can also be
collective anomalies, typically exhibiting abnormal patterns over subsequences.
To address the challenge of collective anomalies, in this paper, we propose a
tree-based MIL framework (TreeMIL). We first adopt an N-ary tree structure to
divide the entire series into multiple nodes, where nodes at different levels
represent subsequences with different lengths. Then, the subsequence features
are extracted to determine the presence of collective anomalies. Finally, we
calculate point-level anomaly scores by aggregating features from nodes at
different levels. Experiments conducted on seven public datasets and eight
baselines demonstrate that TreeMIL achieves an average 32.3% improvement in F1-
score compared to previous state-of-the-art methods. The code is available at
https://github.com/fly-orange/TreeMIL.