Wind Turbine Fault Detection. Newer version @ https://github.com/lkev/wtphm

Code release of paper "MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting" (ICLR 2023)

Air Quality Predictions with a Semi-Supervised Bidirectional LSTM Neural Network

EEMD、LSTM、time series prediction、DO、Deep Learning

Adaptive Multivariate Empirical Mode Decomposition

Multidimensional LSTM for High-Frequency Time Series

implementing a research paper

CNN-Bidirectional LSTM network to forecast long term traffic flow in Madrid.

交通流量预测项目在研，以下是本人学习过程中积累整理的资源，会持续更新

Some TrafficFlowForecasting Solutions(交通流量预测解决方案)

Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).

Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, AAAI 2019, pytorch version

Multi-task LSTMs in Dynet

2024 up-to-date list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.

Multi-Step Spatio-Temporal Forecasting: https://authors.elsevier.com/sd/article/S0306-2619(22)01822-0

Seq2Seq model with data augmentation and data pruning

A new data-driven based architecture (fast-MEEMD) to decompose spatial-temporal data into multi components and a residue

Python implementation of Empirical Mode Decompoisition (EMD) method

🍃 Wind Speed Prediction Model based on Pytorch

For largescale wind power penetration Wind speed prediction is a basic requirement of wind energy generation. There are many artificial neural network (ANN), ARMA, ARIMA approaches proposed in the …

Wind Speed Prediction using LSTMs in PyTorch (https://arxiv.org/pdf/1707.08110.pdf)