LeopardCatCat's Stars
chevalier88/GA_Project_5_Capstone_Multiclass_Legal_Text_Classification_BERT
GA Project 5 (Capstone Project): Using Neural Networks (BERT) with Legal NLP for Contract Clause Classification in real-life clauses
373675032/smart-medicine
智慧医药系统（smart-medicine）是一个基于 SpringBoot 开发的标准 Java Web 项目。整体页面非常的简约大气，整合了目前非常火爆的 AIGC 生成式 AI（选用的阿里的通义千问大语言模型）技术充当智能医生，以此提升系统的 B 格，整体来看是一个偏向百科查询类的系统，功能设计的较为简单，便于初学者理解和学习，也适合学校中的项目答辩或者毕业设计。喜欢的话记得帮忙点亮 Star，不求打赏，免费分享，只求你一个免费的👍，你的支持是我做下去的动力。
xqy0211/Damai-ticket
为了在大麦网抢到周杰伦演唱会的门票，自己闷头学了一会网页的selenium想解放一下生产力，本项目能够实现自动化操作，但在实战中由于自己的电脑配置以及网络原因出现了一些问题。相信解决了这两个问题应该能实现抢票。
AdamManhercz/Bitcoin_stock_predicition
Timeseries forecast on Bitcoin stock price with ARIMA, Prophet, LSTM Recurrent Neural Network and XGBoost.
ramtiin/Predicting-Stock-Prices-Using-FB-Prophet
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. In this notebook I'm going to try forecasting Google stock price using facebook's prophet model.
eervin123/streamlit-prophet
stock or crypto prediction application using fbprophet
nikhils10/Time-Series-Forecasting-Apple-Stock-Price-Using-SARIMA-Prophet
Time Series forecasting using Seasonal ARIMA & Prophet. Applied statistical tests like Augmented Dickey–Fuller test to check stationary of series. Checked ACF ,PACF plots to identify Moving average and Auto-regressive order of series. Transformed series to make it stationary.
krecicki/gluonts-sp500-stock-prediction
GluonTS time series Jupiter notebook for prediction S&P500 daily close price.
hemantnyadav/deepar_gluonts
This is sample implementation of Time Series forecasting using gluonts -mxnet
jsyoon0823/Time-series-prediction
Basic RNN, LSTM, GRU, and Attention for time-series prediction
zhangxu0307/time_series_forecasting_pytorch
time series forecasting using pytorch，including ANN,RNN,LSTM,GRU and TSR-RNN，experimental code
eggegg0419/informer_stock
探究利用informer进行时间序列预测，用于量化分析。实验以2011-01-01至2016- 01-01数据为训练集，2016-01-01至2018-01-01为验证集，2018-01-01至2023-01-01为测试集。Encoder部分输入前63天的数据，decoder部分我们希望根据前21日的数据预测后3日的收益率情况，采用的被解释变量为日收益率，解释变量为日、月、季度、半年、年标准化收益率、MACD_8_24、MACD_16_48、MACD_32_96等8个因子， 最终得到的品种平均IC值为0.0514。
zhouhaoyi/Informer2020
The GitHub repository for the paper "Informer" accepted by AAAI 2021.
liangkaimeng/LstmStockPricePredict
股票价格走势预测、短期升降预测
muatif123/LSTMStockPricePrediction
jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
midas-research/fast-eacl
QSCTech/zju-icicles
浙江大学课程攻略共享计划