945 Open source projects that are alternatives of or similar to Merlion

Luminaire is a python package that provides ML driven solutions for monitoring time series data.

Time Series Ensemble Forecasting

Automated Time Series Forecasting

Time Series Forecasting Best Practices & Examples

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.

A full pipeline AutoML tool for tabular data

An End-to-end Outlier Detection System

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).

Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

Anomaly detection for temporal data using LSTMs

Time Series Benchmark Suite, a tool for comparing and evaluating databases for time series data

Gaussian dynamic Bayesian networks structure learning and inference based on the bnlearn package

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

MSGARCH R Package

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

A Python library for time series forecasting

Anomaly detection for streaming time series, featuring automated model selection.

An open source library for Fuzzy Time Series in Python

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

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

sknifedatar is a package that serves primarily as an extension to the modeltime 📦 ecosystem. In addition to some functionalities of spatial data and visualization.

Code examples for pyFTS

Scalable machine 🤖 learning for time series forecasting.

Forecast time series and stock prices with SCINet

An overview of univariate time series forecasting models with sample code.

Resampling Tools for Time Series Forecasting with Modeltime

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

A python library for easy manipulation and forecasting of time series.

NeuralProphet - A simple forecasting model based on Neural Networks in PyTorch

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 BERT.

StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

An R package with Python support for multi-step-ahead forecasting with machine learning and deep learning algorithms

This repository is the PyTorch implementation of GAN Ensemble for Anomaly Detection.

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

This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the time series forecasting research space.

Probabilistic time series modeling in Python

Time series analysis in Java

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Modeltime unlocks time series forecast models and machine learning in one framework

A Python library for anomaly detection

The AI Application Platform We All Need. Human AND Machine Intelligence. Based on experience building AI solutions at Panasonic: robotics predictive maintenance, cold-chain energy optimization, Gigafactory battery mfg, avionics, automotive cybersecurity, and more.

COVID-19 spread shiny dashboard with a forecasting model, countries' trajectories graphs, and cluster analysis tools

📈 high level wrapper for parallel univariate time series forecasting 📉

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

Detecting and Predicting Forest Cover Change in Pakistani Areas Using Remote Sensing Imagery

GPU-Accelerated Deep Learning for Time Series using Modeltime GluonTS (Learning Lab 53). Event sponsors: Saturn Cloud, NVIDIA, & Business Science.

High-performance time series algorithms for Ruby

Python package for early warning signals (EWS) of bifurcations in time series data.

Python package for automatically constructing features from multiple time series

NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.

CentOS based Docker container for Time Series Analysis and Modeling.

Resources for working with time series and sequence data

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

fast implementation of singular spectrum transformation (change point detection algorithm)

📈 PyTorch dual-attention LSTM-autoencoder for multivariate Time Series 📈

ARCH models in Python

LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION

GluonTS Deep Learning with Modeltime

A repository for code accompanying the manuscript 'An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series' (published at TNNLS)