Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

TODS: An Automated Time-series Outlier Detection System

A toolkit for machine learning from time series

[TPAMI 2024] Awesome Resources of GNNs for Time Series Analysis (GNN4TS)

Awesome Easy-to-Use Deep Time Series Modeling based on PaddlePaddle, including comprehensive functionality modules like TSDataset, Analysis, Transform, Models, AutoTS, and Ensemble, etc., supporting versatile tasks like time series forecasting, representation learning, and anomaly detection, etc., featured with quick tracking of SOTA deep models.

MOMENT: A Family of Open Time-series Foundation Models

ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.

This repository offers a collection of recent time series research papers, including forecasting, anomaly detection and so on , with links to code and resources.

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

Time series anomaly detection algorithm implementations for TimeEval (Docker-based)

Evaluation Tool for Anomaly Detection Algorithms on Time Series

The official code 👩‍💻 for - TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis

Awesome Time-Series and Spatio-Temporal Related

Supporting material and website for the paper "Anomaly Detection in Time Series: A Comprehensive Evaluation"

GutenTAG is an extensible tool to generate time series datasets with and without anomalies; integrated with TimeEval.

[official] PyTorch implementation of TimeVQVAE-AD, a time series anomaly detection model.

CARLA: A self-supervised contrastive learning model for time series anomaly detection. Enhances anomaly detection by learning robust representations of time series data.

PyTorch implementation of "Drift doesn't Matter: Dynamic Decomposition with Dffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection" (NeurIPS 2023)

[Read-Only Mirror] Benchmarking Toolkit for Time Series Anomaly Detection Algorithms using TimeEval and GutenTAG

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