An open-source, low-code machine learning library in Python
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
A unified framework for machine learning with time series
Anomaly detection related books, papers, videos, and toolboxes
A python library for user-friendly forecasting and anomaly detection on time series.
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Merlion: A Machine Learning Framework for Time Series Intelligence
STUMPY is a powerful and scalable Python library for modern time series analysis
List of tools & datasets for anomaly detection on time-series data.
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 🚀.
A curated list of awesome anomaly detection resources
Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。
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
A large collection of system log datasets for AI-driven log analytics [ISSRE'23]
The collection of pre-trained, state-of-the-art AI models for ailia SDK
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
A high-level machine learning and deep learning library for the PHP language.
A machine learning toolkit for log parsing [ICSE'19, DSN'16]
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
Find big moving stocks before they move using machine learning and anomaly detection
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