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2022_wentz_energies

This repository contains the source code for the paper 'Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models

3d-lung-nodules-detection

【医学影像分析】3D-CT影像的肺结节检测（LUNA16数据集）

agricultural-commodities-price-forecasting-using-deep-neural-networks

Forecasting the prices of agricultural commodities using LSTNets

astgcn

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (ASTGCN) AAAI 2019

automatic_hierarchical_forecaster

Automatic Hierarchical Time Series Forecasting using GPs

awesome-forecast-reconciliation

Collection of academic papers, articles, software tools, and educational resources about forecast reconciliation

awesomedeeplearningguidance-timeseriesdata

Application guidance of deep learning models(CNN, RNN, LSTNet and so on) on time series data

basicts

A Standard and Fair Time Series Forecasting Benchmark and Toolkit.

books-free-books

免费书籍汇总。　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　 　 　 　 　 　 　 　

compare-forecast-models

Energy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns.  In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM.  Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R2 score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R2, MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.

crop-yield-prediction-using-machine-learning-ensemble-algorithms

A Crop Yield prediction model which is using Machine Learning Ensemble Regression Algorithms

cv

最全面的 CV 笔记

deep-time-series

Deep learning PyTorch library for time series forecasting

dysat_pytorch

Pytorch implementation of DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks

ensemble-models-ml-dl-

Ensemble models, machine learning, deep learning, optimization

forlazyman.github.io

gluonts-hierarchical-icml-2021

gman-pytorch

Implementation of Graph Muti-Attention Network with PyTorch

hamoye-random-forest

Hamoye Random Forest Group Project for Gold Price Prediction

htsf

Hierarchical Time Series Forecasting

imbalanced-regression

[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression

kaggle-solutions

🏅 Collection of Kaggle Solutions and Ideas 🏅

lithium-ion-rul-prediction

lstm-power-forecasting

This LSTM network serves as a basis for a solar pv power output prediction paper i made back in april 2019.

lstnet

lstnet-1

A Tensorflow / Keras implementation of "Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks" paper

lstnet-with-attention-tf.keras

luna16-lung-nodule-analysis-2016-challenge

LUNA16-Lung-Nodule-Analysis-2016-Challenge

ml-nlp

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

msds696-masters-final-project

Earthquake Prediction Challenge with LightGBM and XGBoost