Popular GitHub Repositories for Time Series Classification

The machine learning toolkit for time series analysis in Python

Deep Learning for Time Series Classification

This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

InceptionTime: Finding AlexNet for Time Series Classification

DEPRECATED, now in sktime - companion package for deep learning based on TensorFlow

Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments

Transfer learning for time series classification

Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.

Python framework for automated time series classification, regression and forecasting

Adversarial Attacks on Deep Neural Networks for Time Series Classification

An official code for paper: TFPred: Learning discriminative representations from unlabeled data for few-label rotating machinery fault diagnosis

Multivariate time series classification with a bi-directional ESN with a deep neural network readout.

Early prediction of sepsis with gradient boosting (XGBoost) and deep learning (LSTM and GRU) using MIMIC-III data.

Repository of paper "TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack" (ECAI'24)

InceptionTime: Finding AlexNet for Time Series Classification

This repository is a PyTorch implementation of NIPS 2019 Paper "Shallow RNNs: A Method for Accurate Time-series Classification on Tiny Devices"

This advanced framework integrates the benefits of an Autoencoder-generated embedding space with the adversarial training dynamics of GANs for time series generation..

Datasets, Transforms and Models specific to Time Series, powered by PyTorch

Machine Learning Transition State Analysis (MLTSA) suite with Analytical models to create data on demand and test the approach on different types of data and ML models.

We introduce an advanced framework that integrates the advantages of an autoencoder-generated embedding space with the adversarial training dynamics of GANs for time series generation.