Official implementation of ECML PKDD'24 paper 'Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection'.

Anomaly Detection for SWaT Dataset using Sequence-to-Sequence Neural Networks

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Code implementation of MTS-DVGAN

Artifact evaluation for "E2Usd: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series" accepted by WWW'24

Source codes for the Coxian Hidden Semi-Markov Models (CxHSMM)

A library for hidden semi-Markov models with explicit durations

An LSTM auto-encoder for Multivariate Cyclic Time Series (MCTS) anomaly detection. The code can be adapted to any MCTS but was applied on Wafer dataset.

A quick tour of generative AI with NNs, MLPs, AE, VAE, GAN, RNN/LSTM, and transformers

Transfer Learning - Sentiment Classification (using LSTM - GRU and Bidirectional RNN for layers)- Unsupervised DL - CNN-AE - DCGAN

Class project for Data Science and Machine Learning course of Università di Salerno Computer Science Master degree, time series clustering achieved through autoencoder's training

ICS anomaly detection test suite. From the ESORICS 2022 paper: "Perspectives from a Comprehensive Evaluation of Reconstruction-based Anomaly Detection in Industrial Control Systems""

Library of ML-based attribution methods for ICS anomaly detection. From the NDSS 2024 paper: "Attributions for ML-based ICS anomaly detection: From theory to practice"

ICS attack simulator for the Tennessee Eastman Process. From the NDSS 2024 paper: "Attributions for ML-based ICS anomaly detection: From theory to practice"

About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_

Python Data Science Handbook: full text in Jupyter Notebooks

No-offical implementation

RNN based Time-series Anomaly detector model implemented in Pytorch.

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

LeetCode刷题记录

Lstm variational auto-encoder for time series anomaly detection and features extraction

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

Implementation of Convolutional LSTM in PyTorch.

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).