romankor-dev

romankor-dev's Stars

• TheAlgorithms/Java

  All Algorithms implemented in Java

  Language:Java58.8k19k

• jenkinsci/jenkins

  Jenkins automation server

  Language:Java23k8.7k

• facebook/react

  The library for web and native user interfaces.

  Language:JavaScript228k46.5k

• vuejs/vue

  This is the repo for Vue 2. For Vue 3, go to https://github.com/vuejs/core

  Language:TypeScript208k33.7k

• apache/dubbo

  The java implementation of Apache Dubbo. An RPC and microservice framework.

  Language:Java40.4k26.4k

• romankor-dev/linux

  Linux kernel source tree

  1

• electron/electron

  :electron: Build cross-platform desktop apps with JavaScript, HTML, and CSS

  Language:C++114k15.3k

• numpy/numpy

  The fundamental package for scientific computing with Python.

  Language:Python27.7k9.9k

• romankor-dev/numpy

  The fundamental package for scientific computing with Python.

  1

• romankor-dev/postgres

  Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch

  1

• romankor-dev/netbeans

  Apache NetBeans

  1

• romankor-dev/kafka

  Mirror of Apache Kafka

  1

• romankor-dev/spring-boot

  Spring Boot

  1

• romankor-dev/jdk

  JDK main-line development https://openjdk.org/projects/jdk

  Language:Java1

• MarcoJHWang/Futures-Price-Prediction

  Trend marketplace Fall project for team 16

  Language:Jupyter Notebook32

• cristianpjensen/stock-market-prediction-via-google-trends

  Attempt to predict future stock prices based on Google Trends data.

  Language:Python9526

• h-sami-ullah/Deep-Learning-for-time-series-forcasting

  Designing a Machine Learning algorithm to predict stock prices is a subject of interest for economists and machine learning practitioners. Financial modelling is a challenging task, not only from an analytical perspective but also from a psychological perspective. After 2008 financial crisis, many financial companies and investors shifted their interest towards predicting future trends. Most of the existing methods for stock price forecasting are modelled using non-linear methods and evaluated on specific data sets. These models are not able to generalize for diverse datasets. Financial time series data is highly dynamic in nature and makes it difficult to analyze through statistical methods. Recurrent Neural Networks (RNN) based Long Short- Term Memory (LSTM) networks were able to capture the patterns of the sequences data meanwhile statistical methods tried to generalize by memorizing data instead of recognizing patterns. In this work, we examined the performance of LSTM model and statistical models over stock prices of different companies to generalize the model. The experimental results of this study show that, LSTM network outperformed traditional statistical methods like ARIMA, MA and AR models. Furthermore, we have noticed that, LSTM network was able to perform consistently on different data sets while statistical methods showed varied performance. Through this project, we addressed the gaps in current models of stock price prediction in both economic and machine learning perspective.

  Language:Jupyter Notebook181

• mikechen66/LSTM-TF2

  The new trend is that the CNN models are merging with the sequence models such as RNN and LSTM. NASNet is a typical example. LSTM will be merging with the CNN models to propel the deep learning development in the future.

  Language:Jupyter Notebook1

• KushBhatt96/Stock-Trend-Predictor-RNN

  This model was trained on 5 years of historical stock market data using an LSTM recurrent neural network. Using this data, it is able to determine the likely future trends of the stock.

  Language:Jupyter Notebook1

• h-pal/Stock-Prices-Trend-Prediction

  Stock prices prediction using LSTM on different datasets

  Language:Jupyter Notebook2

• SanthanamD/Stock-Trend-Prediction-using-LSTM

  Language:Jupyter Notebook1

• STribedi-94/stock-trend-prediction

  Stock Trend Prediction with LSTM Neural Network

  Language:Jupyter Notebook21

• HassanWaheedGithub/LSTM_TREND_PREDICTION

  STOCK MARKET ANALYSIS USING TIME-SERIES FOR OIL AND GAS SECTORS [GUSH] EXCHANGE TRADED FUND (ETF) 30 DAYS TREND PREDICTION

  Language:Jupyter Notebook1

• KalimAmzad/Stock-Trend-Prediction

  Stock Trend Prediction using LSTM

  Language:Jupyter Notebook1

• Ganesh16-dev/Stock_trend_Prediction

  Stock Trend Prediction Using LSTM

  Language:HTML1

• shyamsuthar/LSTM--machine-learning-model

  Stock trend prediction model

  Language:Jupyter Notebook1

• darkspyder/Stock_Trend_Prediction_using_Recurrent_Neural_Networks_LSTM

  Predict stock movements and future trends in the Indian Stock market (eg stock used as SBIN) using a Recurrent Neural Network using LSTM (Long Short Term Memory)

  Language:Jupyter Notebook6

• aayushsoni4/Stock-Trend-Prediction

  Predict stock price trends with LSTM neural networks and present results through an interactive Streamlit web app.

  Language:Jupyter Notebook5

• Indrajeet-01/Stock-Trend-Prediction

  LSTM

  Language:Jupyter Notebook2

• neurogen-dev/NeuroAPI

  Free ChatGPT 3.5 / Cheap ChatGPT 4o API / Бесплатный ChatGPT 3.5 Turbo / Дешевый СhatGPT 4o

  Language:TypeScript1.3k164