Building Time series forecasting models, including the XGboost Regressor, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), CNN-LSTM, and LSTM-Attention. Additionally, hybrid models like GRU-XGBoost and LSTM-Attention-XGBoost for Electricity Demand and price prediction

Price Prediction Case Study predicting the Bitcoin price and the Google stock price using Deep Learning, RNN with LSTM layers with TensorFlow and Keras in Python. (Includes: Data, Case Study Paper, Code)

Projects include the application of transfer learning to build a convolutional neural network (CNN) that identifies the artist of a painting, the building of predictive models for Bitcoin price data using Long Short-Term Memory recurrent neural networks (LSTMs) and a tutorial explaining how to build two types of neural network using as input the MNIST dataset, namely, a CNN using Keras and a fully-connected network using TensorFlow.

I have implemented Recurrent Neural Network (RNN model) to predict the future stock prices and compare it with linear regression.

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.

Context A real online retail transaction data set of two years. Content This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers. Column Descriptors InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (£). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides. Acknowledgements Here you can find references about data set: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II and Relevant Papers: Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018. Inspiration This is Data Set Characteristics: Multivariate, Sequential, Time-Series, Text

This project uses a Long Short-Term Memory (LSTM) model, a type of recurrent neural network, to predict the future price of Bitcoin.

The goal of this project was to predict stock market prices using a recurrent neural network. This project was inspired by the Standford paper "Financial Market Time Series Prediction with Recurrent Neural Networks Armando Bernal, Sam Fok, Rohit Pidaparthi December 14, 2012" . Using the Standford paper as a baseline, I was able to improve and beat the accuracy of their model using hyper parameter tuning of my RNN network.

In this repository, based on the latest NLP pappers, we researched on sequential data and time series and developed tasks in NLP such as stock price prediction, time series prediction, sentiment analysis from text and We developed the language model and so on. This research is based on recurrent neural networks, LSTM networks and the new Transformer architecture and attention mechanism.

In this project, I created prediction model for predict bitcoin price with Gated Recurrent Unit Model. GRU is a gating mechanism in recurrent neural networks (RNN) similar to a long short-term memory (LSTM), GRU have more simple computation and faster than LSTM because have fewer number of gates.

This is a supervised Recurrent Neural Network (RNN) learning project treating stock trading as a classification problem. Given input of a 60 day window of pricing data, choose the best action for maximum profit. This uses my earlier https://github.com/TimRivoli/Stock-Price-Trade-Analyzer project for a trading environment, and its SeriesPrediction module for data preparation and model training.

I built a deep learning model (Recurrent Neural Network ) to predict Henry Hub Natural Gas Spot Price.

An exploration into Recurrent Neural Networks using LSTM. The implmentation is named Cryptonet as it attempts to model trends in the price of bitcoin of varying lengths. Results of the experiment can be found within the results folder.

Prediction of Stock price using Recurrent Neural Network (RNN) models. Contains GRU, LSTM, Bidirection LSTM & LSTM combinations with GRU units. The models were deveoped using the keras module from Tensorlfow.

Recurrent Neural Networks (RNNs) are used for real-time cryptocurrency price prediction, aiming to outperform traditional methods in the volatile crypto market. Here, three DL methods are compared to find out which RNN model gives the best result.

This project focuses on predicting stock prices using Recurrent Neural Networks (RNNs), a type of deep learning model well-suited for sequential data. The provided Jupyter notebook includes all the steps necessary for data preprocessing, model training, evaluation, and making predictions on stock market data.

This project implements a stock price prediction model using a Recurrent Neural Network (RNN) in Python. The model is trained on historical stock price data to predict future prices. The key steps include data preprocessing, model training, and visualization of predictions.

This paper proposes a novel hybrid approach for stock price prediction. Stock price prediction implies telling whether a particular stock price will go up or down on a certain day. The model proposed in this paper uses Gated Recurrent Units to take into account the sequential nature of days, which is not handled by CNNs. However, the CNN is used to capture relationships between long term and short term stock patterns. Finally we have a simple feedforward neural network which learns the weights to be given to the CNN prediction and the weight to be given to the GRU prediction.

in this repository we intend to predict Google and Apple Stock Prices Using Long Short-Term Memory (LSTM) Model in Python. Long Short-Term Memory (LSTM) is one type of recurrent neural network which is used to learn order dependence in sequence prediction problems. Due to its capability of storing past information, LSTM is very useful in predicting stock prices.

Modeling predictions of closing prices for ETFs tracking S&P 500 and Gold using a Random Forest Algorithm and a Recurrent Neural Network

This project predicts stock prices using a Long Short-Term Memory (LSTM) neural network. It fetches historical stock data using Yahoo Finance API, preprocesses data for training, trains an LSTM model, a recurrent neural network (RNN), for prediction, and visualizes actual vs. predicted stock prices.

Deep learning recurrent neural networks to model bitcoin closing prices. One model uses the FNG indicators to predict the closing price while the second model uses a window of closing prices to predict the nth closing price.

This project implements a Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) layers to predict stock prices based on historical data. The model is trained on stock price data obtained from Yahoo Finance and evaluates its performance using various metrics.

A Django web application which scrapes data from Yahoo Finance and displays on a user friendly platform. Additional, Using a Recurrent Neural Network Long Short Term Memory Model (LSTM), predicts future stock prices.

LSTM based RNN(Recurrent Neural Network) model to predict stock price w/ dataset extractor for nepali company stock prices

This project explores using a deep learning recurrent neural network (RNN) to model bitcoin closing prices.

An easy to use program for predicting the Stock prices using recurrent neural networks with LSTM Sequential model.

This project aims to predict price movement of a designated stock during a fixed time frame using neural network models. The motivation comes from the increasing usage of electronic-trading platform in day-to-day trading activities, whereas the automation of movement of mid-price and price spread crossing becomes an essential part of every-day trading mechanism. By characterising the existing features in given dataset and creating new statistical features such as moving averages, we applied feed forward neural network (FFNN), convolutional neural network (CNN) and recurrent neural network (RNN) on training dataset and tested on validation dataset. After a comparison and discussion of the accuracies and losses of all three models, we reach to a conclusion that FFNN model works the best, with a training accuracy at 0.531 and test accuracy at 0.5, as well as a training loss level at 1.038 and test loss level at 1.04.

Using deep learning recurrent neural networks to model bitcoin (BTC) closing prices with two models. One model will use the Fear and Greed Index (FNG) to predict the closing price while the second model will use a window of closing prices to predict the nth closing price.

This repository contains code for predicting stock market prices using Recurrent Neural Networks (RNN). The model is trained on historical stock data and can be used to make predictions for future stock prices.