一种可区分四类异常的无监督时序检测框架，自动识别并解释异常类型。
CRAFTIIF: Cross-Resolution Analytic Four-Type Interpretable Isolation Forest for Multivariate Time Series Anomaly Detection
- 用四种小波特征生成500组随机特征，分别对应四类异常
- 在19个数据集上平均F1达0.228，领先第二名40.7%的性能
- 无需调参，自动适配异常率，且每条分支直接给出异常类型归属
多变量时间序列异常检测面临四类结构迥异的异常：点异常（孤立尖峰）、分布异常（水平偏移）、时序异常（节奏变化）和集体异常（传感器间相关性崩溃），每类需不同特征表示。现有无监督方法通常仅针对一到两类，且可解释性差。本文提出CRAFTIIF（跨分辨率分析四类可解释隔离森林），一个全无监督框架，无需数据集调参即可同时检测四类异常。CRAFTIIF在四个小波族（Morlet、DOG、Haar、Coiflet）中生成K=500组随机解析小波特征，每类特征专攻一类异常，输入五个结构化隔离森林——每类异常一个，外加一个元隔离森林处理复合异常。自适应Otsu/MAD阈值实现从0.1%到69.2%异常率下的自动校准。由于每个隔离森林仅使用特定类型特征训练，分支触发天然提供异常类型归属，无需事后解释。在mTSBench基准全部19个数据集上评估，平均F1为0.228（所有19个），在13个可检测数据集上为0.322，在VUS-PR指标上以0.463排名第一（前一最佳0.329，提升40.7%）。诊断框架（最优F1、可检测性极限、分支分离比）识别出其中6个数据集本质上无法被任何无监督方法检测。11种条件消融实验验证自适应阈值（F1+38%）、四分支结构（+20%）和元隔离森林（+23%）均至关重要。
原文摘要 · Abstract (English)
Anomaly detection in multivariate time series is challenged by four structurally distinct anomaly types -- point (isolated spikes), distributional (level shifts), temporal (rhythm changes), and collective (inter-sensor correlation breakdowns) -- each requiring different feature representations. Most unsupervised methods target only one or two types and provide limited interpretability. We present CRAFTIIF (Cross-Resolution Analytic Four-Type Interpretable Isolation Forest), a fully unsupervised framework targeting all four types without dataset-specific tuning. CRAFTIIF generates K=500 random analytic wavelet feature draws across four families (Morlet, DOG, Haar, Coiflet), each targeting a specific anomaly type, feeding five structured Isolation Forests -- one per type plus a meta-IF for compound anomalies. An adaptive Otsu/MAD threshold calibrates detection automatically across anomaly rates from 0.1% to 69.2%. Because each IF is trained exclusively on type-specific features, branch firing provides direct anomaly-type attribution by construction, without post-hoc explanation. Evaluated on all 19 datasets of the mTSBench benchmark (Zhou et al., TMLR 2026), CRAFTIIF achieves mean F1=0.228 (all 19 datasets) and F1=0.322 (13 detectable datasets), ranking first among all 25 evaluated methods on VUS-PR (0.463 vs. previous best 0.329, +40.7%). A diagnostic framework -- oracle F1, detectability limits, and branch separation ratios -- identifies 6 of 19 datasets as fundamentally undetectable by any unsupervised method. Ablation over 11 conditions confirms adaptive thresholding (+38% F1), four-branch structure (+20%), and meta-IF (+23%) are each essential. Code: https://github.com/smitswil/craftiif
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