The project focused on the use of public data to assess the economic situation in the country based on the state of the stock market and national means of payment, in particular - of the national currency. As sources are used: Open data Ministry of Finance of the Russian Federation These Moscow Exchange Google Finance Data Technologies used: Backend: Databases (relational) - Microsoft SQL Server 2014 Databases (multivariate) models DataMining, OLAP-cube - Microsoft Analysis Services 12.0 Веб-сервер - Windows Server 2012 / Internet Information Services Самописный ASP.NET HTTP Restful интерфейс для взаимодействия с Frontend ETL (загрузка и пре-процессинг данных, управление обновлением данных) SQL Server Integration Services 2014 (разработка в Visual Studio 2013, SSDT) Frontend: AngularJS ChartJS Twitter Bootstrap These were chosen so that the detail (granularity) in the set is not less than 1 day. The result has been created and filled with data analytic repository (Kimball model, topology - star), which was used to build a multi-dimensional databases and OLAP-based cubes on it, as well as models of analysis of data on two main algorithms: Microsoft Time Series, Microsoft Neural Network . To ensure interoperability frontend and backend server for backend-server was set up HTTP-Restful interface JSON-issuing documents in the form of finished sets. The project includes two main areas: Intelligent visualization of open data Analysis of open data and the construction of forecasts based on them Intelligent visualization involves the use of MDX-queries to the OLAP-cube, followed by depression (drilldown) in the data, the system allows the user to quickly find the "weak points" of the economy, as part of the data collected. To predict the time a standard mix of algorithms ARTXP / ARIMA, without the use of queries involving cross-prediction (but it is possible to enroll in the system correct data). These algorithms have been tested primarily on foreign exchange rates (US dollar) and the assets of banks included in the special list of Ministry of Finance. In addition, for assets shows the different customization options algorithms - a long-term, short-term and medium-term (balanced) plan. Assessing the impact of oil prices and foreign currency exchange rate for the total market capitalization was conducted on a sample of the data collected: companies with a total market capitalization of 100 to 500 million rubles, present in the market during 2013-2015 Analytical server builds the neural network receiving the input exchange rates, companies, the weighted average share price, total capitalization of the company and the price of oil to requests received models give the opportunity to evaluate the growth rate of \ fall (if at all) the company's capitalization at historical exchange rates and / or the cost of oil. Built a system can expand to include new indicators, which will significantly increase the accuracy of forecasting.

This project implements a time series multivariate analysis using RNN/LSTM for stock price predictions. A deep RNN model was created and trained on five years of historical Google stock price data to forecast the stock performance over a two-month period.

Market Risk Management with Time Series Prediction of Stock Market Trends using ARMA, ARIMA, GARCH regression models and RNN for time series analysis and prediction of short-term tends in stock prices.

This project would demonstrate the following capabilities: 1. Extraction Loading and Transformation of S&P 500 data and company fundamentals. 2. Exploratory and Time Series Data Analysis on top of the stock data. 3. Stock Screener based on fundamentals. 4. Stock Price Prediction using multiple and/or an ensemble of machine learning models.

Financial time series analysis and prediction have become an important area of re- search in today's world. Designing and pricing securities, construction of portfolios and other risk management strategies depends on the prediction of financial time se- ries. A financial time series often involve large dataset with complex interaction among themselves. A proper analysis of this data will give the investor better gains, but the existing methodologies focus on linear models (AR, MA, ARMA, ARIMA) and non- linear models (ARCH, GARCH, TAR). These models are not capable of identifying the complex interactions and latent dynamics existing within the data. Applying Deep learning methods to these types of data will give more accurate results than the existing methods. Deep learning architectures can identify the hidden patterns in the data and is also capable of exploiting the interactions existing within the data, which is, at least not possible by the existing financial models. The proposed work uses four different deep learning architectures (RNN, LSTM, CNN, and MLP) for predicting the minute wise stock price for NSE listed companies and compares the performance of the mod- els. The proposed method uses a sliding window based approach for predicting future values on a short-term basis. The performance of the models was quantified using error percentage.

Forecast Apple stock prices using Python, machine learning, and time series analysis. Compare performance of four models for comprehensive analysis and prediction.

This project is a stock price prediction model for Apple Inc. (AAPL) using historical stock price data. The goal is to build a predictive model that can forecast Apple's stock prices for a specified time horizon. The project focuses on time series analysis, machine learning, and data visualization.

Stock_Market_Prediction using textual analysis : Applying the NLP processing and the Sentiment analysis to the textual dataset(news headlines) * Numericel analysis : Applying the numerical analysis to the historical stock prices dataset * merging the two datasets : Create a hybrid model for stock price/performance prediction using deep learning LSTM for time series forcasting with the avoidance of overfitting

A deep learning-based stock price prediction model using LSTM and GRU networks. It uses historical stock data to forecast future prices, focusing on time-series analysis and evaluation metrics like RMSE and MAE to assess model accuracy.

We make use of time series to predict the future values of the Honeywell stock. We perform exponential smoothing forecast on Honeywell stock prices with varying value of parameters to find the best fit. To find the best fit we make use of SSE and MSE and compare all the values. We also perform the prediction using linear regression analysis and compare the results with exponential smoothing forecast. We find the coefficient of correlation and determination. We learn more about the residuals and their shapes when used in scatterplots. We also find the actual Honeywell stock price and compare it with all our forecasts. More information updated in the word file.

• Weather Forecasting is the process of making predictions of the future, based on past and present data of the weather. • We used ARIMA model(Auto Regressive Integrated Moving Average) to analyze and predict the time-series data and we shall also perform rigorous exploratory data analysis and visualizations on the dataset. • Feature Engineering – selecting required attributes. • Data cleaning – renaming attributes and filling missing data. • Check rolling mean and standard deviation (graph must not vary too much for stationarity). • Perform Augmented Dickey–Fuller test (to check for stationarity) • plotting PACF(partial auto correlation function) and ACF(auto correlation function) to find p and q values of ARIMA model. • Fitting and forecasting the model for temperature data. • This could be also be used other types of time series data such as stock prices, market price variations, etc.

This project provides an overview of utilizing deep learning techniques, specifically LSTM neural networks, for stock price prediction through time series analysis. It outlines the significance, proposed solution, challenges, and references, setting the stage for further exploration and implementation in the financial domain.

An Arima model of stock price prediction by time series analysis using R.

Leverages Long Short-Term Memory (LSTM) neural networks to forecast stock prices using historical data. This project showcases how deep learning can be applied to time series analysis for technical stock market prediction, using one year of data from TataGlobal (NSE) as a case study.

No description available

Stock price prediction using time series analysis by LSTM neural network.

Stock-price prediction project for Bupa Arabia (8210) using Python, ML models, feature engineering, and time-series analysis.

Built a Stock Price Predictor that predicts Apple stock prices for a week using Recurrent Neural Network Regression using time series analysis method with 0.016 error margin. Employed the LTSM architecture for RNN since that optimizes classification and predictions when using time series data to avoid issues arising from potentially unknown discrepancies in events in data based on time series.

In this project, I ventured into the exciting world of time series analysis and deep learning to build a robust Stock Price Prediction model using Long Short-Term Memory (LSTM) networks.

Stock Market prediction tool leveraging Hidden Markov Models (HMM) for time series analysis and pattern recognition in stock prices. Focused on Apple stock, the model uses fractional price changes for training, automated data fetching from Yahoo Finance, and MSE for performance evaluation. Visualizes predicted vs actual trends with matplotlib.

Abstract In this project we propose a learning-based stock market information system for stock prices prediction and customer satisfaction estimation to help stock market clients decide their investment. For future prices prediction, we use time series models that are trained using historical stock data of different companies. Particularly, ARIMA model is used for monthly prediction and FBprophet model is used for daily prediction. For customer satisfaction estimation, we analyze and process customer reviewing tweets using sentiment analysis and natural language processing to predict a score indicating the percentage of satisfaction about certain company. Also, in this project, we propose an enhanced approach that combines customer reviews with historical data to improve stock prices prediction using convolutional neural networks or polynomial regression. Model results are analyzed, and comparisons are made between different approaches to determine best model to be adopted for each problem. Finally, we develop a web application with suitable user interface and expressive analyzing graphs for convenient use of our system. As a proof of concept, all of our work is practically applied on stocks and reviewing tweets of different ten global companies.

No description available

Taken a time series data from Bajaj Finance company and predicted the upcoming VWAP prices

No description available

Time-series forecasting project using LSTM neural networks to predict stock prices from historical market data with Streamlit deployment.

Used Historical data of stock prices of 3 companies listed in National Stock Exchange (NSE), predicted the future stock price for the companies.

No description available

It developed by using Python and LSTM Model •This project aims to develop an LSTM-based system to predict stock closing prices of Yahoo Finance using historical data. •Libraries used by yfinance, TensorFlow, sklearn, matplotlib

This repository contains code and examples for predicting stock prices using various time series forecasting models, including ARIMA, SARIMA, and Exponential Smoothing. The code demonstrates data preprocessing, model fitting, prediction, and evaluation.

No description available