A unified toolbox for machine learning with time series

A machine learning toolkit dedicated to time-series data

Deep Learning for Time Series Classification

List of papers, code and experiments using deep learning for time series forecasting

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)

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

Practical Deep Learning for Time Series / Sequential Data library based on fastai v2/ Pytorch

Transfer learning for time series classification

InceptionTime: Finding AlexNet for Time Series Classification

sktime companion package for deep learning based on TensorFlow

Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments

Library for implementing multivariate time series classifiers based on reservoir computing (echo state network)

Data augmentation using synthetic data for time series classification with deep residual networks

Deep Neural Network Ensembles for Time Series Classification

ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

Voice Activity Detection based on Deep Learning & TensorFlow

TSrepr: R package for time series representations

A very lightweight web application for brushing labels onto time series data; useful for building training sets.

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Official TensorFlow\Keras implementation for our NeurIPS 2019 paper, Diffeomorphic Temporal Alignment Nets. PyTorch supprot Coming soon.

PyTorch implementation of the model presented in "Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention"

Analyzes chronological patterns present in time-series data and provides human-readable descriptions

Different deep learning architectures are implemented for time series classification and prediction purposes.

Classification of Time-series data with RNN

A Wasserstein Subsequence Kernel for Time Series.

My Experiments with Time Series

Evaluating surgical skills from kinematic data using convolutional neural networks

InceptionTime: Finding AlexNet for Time Series Classification

A PyTorch implementation of the Light Temporal Attention Encoder (L-TAE) for satellite image time series. classification

Implements High-Gamma dataset decoding using Filter Bank Common Spatial Pattern with rLDA classification and Neural Networks.