Pinned Repositories
-VMD
使用vmd算法对含有噪声的图像信号进行分解，去除掉噪声信号，将剩余信号合成，得到去噪声图像。分别使用alo、ao、ga、gwo、mpa、spo、woa算法对vmd算法中的参数进行优化，实现快速、准确的完成图像信号的分解。
A-stock-prediction-algorithm-based-on-machine-learning
（陆续更新）重新整理过的基于机器学习的股票价格预测算法，里面包含了基本的回测系统以及各种不同的机器学习算法的股票价格预测，包含：LSTM算法、Prophet算法、AutoARIMA、朴素贝叶斯、SVM等
ADGAT
Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks
annotated_deep_learning_paper_implementations
🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
CEEMDAN_LSTM
CEEMDAN_LSTM is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM.
CoST
PyTorch code for CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (ICLR 2022)
Deep-Adaptive-Wavelet-Network
It contains the code for the paper Deep Adaptive Wavelet Network
DeepLearning_Wavelet-LSTM
LSTM + Wavelet（长短期记忆神经网络+小波分析）：深度学习与数字信号处理的结合
DeepLearningForTSF
深度学习以进行时间序列预测
EGE-UNet
This is the official code repository for "EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation".
jiawei0322's Repositories
jiawei0322/-VMD
使用vmd算法对含有噪声的图像信号进行分解，去除掉噪声信号，将剩余信号合成，得到去噪声图像。分别使用alo、ao、ga、gwo、mpa、spo、woa算法对vmd算法中的参数进行优化，实现快速、准确的完成图像信号的分解。
jiawei0322/A-stock-prediction-algorithm-based-on-machine-learning
（陆续更新）重新整理过的基于机器学习的股票价格预测算法，里面包含了基本的回测系统以及各种不同的机器学习算法的股票价格预测，包含：LSTM算法、Prophet算法、AutoARIMA、朴素贝叶斯、SVM等
jiawei0322/ADGAT
Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks
jiawei0322/annotated_deep_learning_paper_implementations
🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
jiawei0322/CEEMDAN_LSTM
CEEMDAN_LSTM is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM.
jiawei0322/CoST
PyTorch code for CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (ICLR 2022)
jiawei0322/Deep-Adaptive-Wavelet-Network
It contains the code for the paper Deep Adaptive Wavelet Network
jiawei0322/DeepLearning_Wavelet-LSTM
LSTM + Wavelet（长短期记忆神经网络+小波分析）：深度学习与数字信号处理的结合
jiawei0322/DeepLearningForTSF
深度学习以进行时间序列预测
jiawei0322/EGE-UNet
This is the official code repository for "EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation".
jiawei0322/EMD-SVR-and-VMD-SVR-stock-index-prediction
jiawei0322/ETDataset
The Electricity Transformer dataset is collected to support the further investigation on the long sequence forecasting problem.
jiawei0322/Learnable-Wavelet-Transform
Code tu use learnable wavelet transforms like L-WPT and DeSPaWN methods in pytorch
jiawei0322/leetcode
jiawei0322/LSTM
基于LSTM的时间序列预测研究
jiawei0322/LSTM_stock
基于LSTM的股票数据分析，数据来源于Tushare
jiawei0322/LSTNet
jiawei0322/MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
jiawei0322/MonthlyRunoffForecastByAutoReg
jiawei0322/multivariate-time-series-data
jiawei0322/N-BEATS
N-BEATS is a neural-network based model for univariate timeseries forecasting. N-BEATS is a ServiceNow Research project that was started at Element AI.
jiawei0322/n-beats-1
Keras/Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
jiawei0322/nbeats-
jiawei0322/nbeats_forecast
N-Beats library implementation
jiawei0322/nbeatsx
jiawei0322/RobustSTL
Unofficial Implementation of RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019)
jiawei0322/SCNN
jiawei0322/vaderSentiment
VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.