A python library for user-friendly forecasting and anomaly detection on time series.

TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.

List of tools & datasets for anomaly detection on time-series data.

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

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values

TODS: An Automated Time-series Outlier Detection System

A toolkit for time series machine learning and deep learning

Unsupervised time series anomaly detection library

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

A Python toolkit for rule-based/unsupervised anomaly detection in time series

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

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

KDD 2019: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

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

MOMENT: A Family of Open Time-series Foundation Models, ICML'24

Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021)

Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".

Applied generative adversarial networks (GANs) to do anomaly detection for time series data

We propose a VAE-LSTM model as an unsupervised learning approach for anomaly detection in time series.

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.

An Integrated Experimental Platform for time series data anomaly detection.

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

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).

SKAB - Skoltech Anomaly Benchmark. Time-series data for evaluating Anomaly Detection algorithms.

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

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

Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

Probabilistic anomaly detection for time series data