Project Name	Stars	Packages Using This	Most Recent Commit	Total Releases	Latest Release	Open Issues	License	Language
Gluonts	4,288	16	7 days ago	107	December 07, 2023	385	apache-2.0	Python
Probabilistic time series modeling in Python
Neural_prophet	3,512	3	3 months ago	24	September 19, 2023	45	mit	Python
NeuralProphet: A simple forecasting package
Pytorch Forecasting	3,439	10	3 months ago	34	July 26, 2020	464	mit	Python
Time series forecasting with PyTorch
Neuralforecast	2,121	5	3 months ago	20	October 05, 2023	98	other	Python
Scalable and user friendly neural :brain: forecasting algorithms.
Time Series Prediction	762		4 months ago	11	October 16, 2023	11	mit	Python
tfts: Time series deep learning models in TensorFlow
N Beats	686		a year ago				mit	Python
Keras/Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
Pypots	558		3 months ago	20	November 30, 2023	14	bsd-3-clause	Python
A Python toolbox/library for reality-centric machine learning/deep learning on partially-observed time series with PyTorch, including SOTA models supporting tasks of imputation, classification, clustering, and forecasting on incomplete (irregularly-sampled) multivariate time series with NaN missing values/data. https://arxiv.org/abs/2305.18811
Timeseries_seq2seq	362		5 years ago			9		Jupyter Notebook
This repo aims to be a useful collection of notebooks/code for understanding and implementing seq2seq neural networks for time series forecasting. Networks are constructed with keras/tensorflow.
Da Rnn	242		3 years ago			20		Python
Dual-Stage Attention-Based Recurrent Neural Net for Time Series Prediction
Traffic_prediction	237		6 months ago				mit	TeX
Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

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Gluonts ⭐ 4,288

Probabilistic time series modeling in Python

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Neural_prophet ⭐ 3,512

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Time Series Prediction ⭐ 762

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Pypots ⭐ 558

A Python toolbox/library for reality-centric machine learning/deep learning on partially-observed time series with PyTorch, including SOTA models supporting tasks of imputation, classification, clustering, and forecasting on incomplete (irregularly-sampled) multivariate time series with NaN missing values/data. https://arxiv.org/abs/2305.18811

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Da Rnn ⭐ 242

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Traffic_prediction ⭐ 237

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most recent commit 6 months ago

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