Anomaly detection related books, papers, videos, and toolboxes

A unified framework for machine learning with time series

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

STUMPY is a powerful and scalable Python library for modern time series analysis

The machine learning toolkit for time series analysis in Python

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.

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

A Python package for time series classification

AIOps学习资料汇总，欢迎一起补全这个仓库，欢迎star

[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"

TODS: An Automated Time-series Outlier Detection System

[ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"

Python AutoML for Trading Systems and Sports Betting

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

A toolkit for machine learning from time series

时间序列分析教程

Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation

Highly comparative time-series analysis