Code for *ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models*

Pytorch implementation of GRU-ODE-Bayes

PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting

Implementation of Convolutional LSTM in PyTorch.

pytorch implemention of trajGRU.

Source code of paper "[NIPS2017] Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model"

ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST

convolutional lstm implementation in pytorch

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)

Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks" at ICLR 2022

pytorch implement of ICASSP2022 "MoDeRNN: Towards Fine-grained Motion Details for spatiotemporal predictive learning"

pytorch implement of ICME2022 "CMS-LSTM: Context Embedding and Multi-Scale Spatiotemporal Expression LSTM for Predictive Learning"

The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“. (NeurIPS 2022)

Implementation of bi-directional Conv LSTM and Conv GRU in PyTorch.

Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.

traffic flow prediction

PyTorch Implementation of Google Research's MetNet and MetNet-2

Graph-based weather forecasting models. Originally, PyTorch implementation of Ryan Keisler's 2022 "Forecasting Global Weather with Graph Neural Networks" paper (https://arxiv.org/abs/2202.07575)

Repository of Transformer based PyTorch Time Series Models

A benchmark dataset for data-driven weather forecasting

Initial public release of code, data, and model weights for FourCastNet

[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

The official implementation of the CVPR'22 paper SimVP: Simpler Yet Better Video Prediction.

Implicit Stacked Autoregressive Model for Video Prediction (official implementation)

FAIR Sequence Modeling Toolkit 2

The best repository showing why transformers might not be the answer for time series forecasting and showcasing the best SOTA non transformer models.