基于图神经网络Graph Neural Network相关的异常检测文章_a survey on graph neural networks for time series:

Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection. CVPR, 2019.

Towards open set video anomaly detection. ECCV, 2019.

Decoupling representation learning and classification for GNN-based anomaly detection. SIGIR, 2021.

Crowd-level abnormal behavior detection via multi-scale motion consistency learning. AAAI, 2023.

Rethinking graph neural networks for anomaly detection. ICML, 2022。

Cross-domain graph anomaly detection via anomaly-aware contrastive alignment. AAAI, 2023.

A causal inference look at unsupervised video anomaly detection. AAAI, 2022.

NetWalk: A flexible deep embedding approach for anomaly detection in dynamic networks. KDD, 2018.

LUNAR: Unifying local outlier detection methods via graph neural networks. AAAI, 2022.

Series2Graph: Graph-based subsequence anomaly detection for time series. VLDB, 2022.

Graph embedded pose clustering for anomaly detection. CVPR, 2020.

Fast memory-efficient anomaly detection in streaming heterogeneous graphs. KDD, 2016.

Raising the bar in graph-level anomaly detection. IJCAI, 2022.

SpotLight: Detecting anomalies in streaming graphs. KDD, 2018.

Graph anomaly detection via multi-scale contrastive learning networks with augmented view. AAAI, 2023.

Counterfactual graph learning for anomaly detection on attributed networks. TKDE, 2023.

Deep variational graph convolutional recurrent network for multivariate time series anomaly detection. ICML, 2022.

SAD: Semi-supervised anomaly detection on dynamic graphs. arXiv, 2023.

Improving generalizability of graph anomaly detection models via data augmentation. TKDE, 2023.

Anomaly detection in networks via score-based generative models. ICML, 2023.

Generated graph detection. ICML, 2023.

Graph-level anomaly detection via hierarchical memory networks. arXiv, 2023.

CSCLog: A component subsequence correlation-aware log anomaly detection method. arXiv, 2023.

A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection. arXiv, 2023.

mbd.pub/o/GeBENHAGEN

擅长现代信号处理(改进小波分析系列，改进变分模态分解，改进经验小波变换，改进辛几何模态分解等等)，改进机器学习，改进深度学习，机械故障诊断，改进时间序列分析(金融信号，心电信号，振动信号等)

import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt 
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor