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使用LSTM对股票价格进行回归预测，对股价涨跌进行分类预测。We use LSTM to forecast the stock price and classify the rise and fall of the stock price.
xi correlation method adapted for python
Algorithm for time series clustering
The official implementation of the paper: "SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting"
The PyTorch implementation of "Modeling Financial Time Series using LSTM with Trainable Initial Hidden States"
Python implementations of the Boruta all-relevant feature selection method.
NN models designed to analyze and predict stock market from any stock data given
Transformer and MultiTransformer layers for stock volatility forecasting purposes
An Ensemble DL Model Tuned with Genetic Algorithm for Oil Production Forecasting.
StockFormer: A Swing Trading Strategy Based on STL Decomposition and Self-Attention Networks
Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction
This is the official code and supplementary materials for our AAAI-2024 paper: MASTER: Market-Guided Stock Transformer for Stock Price Forecasting. MASTER is a stock transformer for stock price for…
Minimal, clean example of lstm neural network training in python, for learning purposes.
A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.
PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series
CNN+BiLSTM+Attention Multivariate Time Series Prediction implemented by Keras
一些用C编写的小游戏, 14行贪吃蛇 22行2048 22行俄罗斯方块 25行扫雷...以及各种小玩意
C语言教程+博客+代码演示+课程设计。 帮助初学者更好的理解 C 难点，提升代码量！ For beginners:C tuition/self-learning
LSTM and XGboost are used to forecast multi-factor convertible bonds and stocks
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
Let's reproduce paper simulations of multi-robot systems, formation control, distributed optimization and cooperative manipulation.
《C++ Primer Plus 第6版（中文版）》原书代码、习题答案和个人笔记，仅供学习和交流。
Predict operation stocks points (buy-sell) with past technical patterns, and powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow…