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 …

TODS: An Automated Time-series Outlier Detection System

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.

📈 Awesome resources related to GNNs for Time Series Analysis (GNN4TS) 🔥 https://arxiv.org/abs/2307.03759

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

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

Awesome Time-Series and Spatio-Temporal Related

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

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

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

Precursor-of-Anomaly Detection

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

Final Project for the 'Machine Learning and Deep Learning' Course at AGH Doctoral School

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

Methodology for anomaly detection on multivariate streams using path signatures and the variance norm.

Time Series Forecasting using RNN, Anomaly Detection using LSTM Auto-Encoder and Compression using Convolutional Auto-Encoder

Time series anomaly detection, time series classification & dynamic time warping, performed on a dataset of Canadian weather measurements.

Python package for Singular Spectrum Analysis