Search Results for "artificial intelligence java source code"

NeuralProphet bridges the gap between traditional time-series models and deep learning methods. It's based on PyTorch and can be installed using pip. A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch. You can find the datasets used in the tutorials, including data preprocessing examples, in our neuralprophet-data repository. The documentation page may not we entirely up to date. Docstrings should be reliable, please refer to those when in...

Quadratic enables your team to work together on data analysis to deliver better results, faster. You already know how to use a spreadsheet, but you’ve never had this much power before. Quadratic is a Web-based spreadsheet application that runs in the browser and as a native app (via Electron). Our goal is to build a spreadsheet that enables you to pull your data from its source (SaaS, Database, CSV, API, etc) and then work with that data using the most popular data science tools today...

Venice is a stock market trading programme that supports portfolio management, charting, technical analysis, paper trading and genetic programming. Venice runs in a graphical user interface with online help and has full documentation.

This is the open/public source code repository for the Java programs shown in the YouTube videos - A.I. Stock Trends With WEKA, TA-Lib and more https://www.youtube.com/channel/UCPxmgFZDS7F06UBBxH5b4mg

Cenobi is designed for management accountants, not (only) for statisticians and data mining experts. Carefully arranged default settings make sure you can concentrate on Cenobi's many accounting features rather than worrying about setting up artificial neural networks or genetic algorithms, which are the main machine learning tools under Cenobi's hood. Cenobi's main benefits are: - ease of use - Utilizing artificial neural networks to estimate cost relationships, Cenobi is able to...

R-Portfolio - breakeven optimally diversified investment portfolio. Оптимально диверсифицированный инвестиционный портфель.

A graphical platform for monitoring the US stock market using OpenTick. Our focus is to have a simple and intuitive interface yet providing the commonly used tools (technical analysis, plots,...).

Complete technical analysis & trading system, full set of features: retrieve, analyze EOD stocks data; manage multiple portfolios; technical analysis & graphical rendering; neural networks for generation of trading signals; support trader community

The aim of the project is to build multi-agent distributed system for modeling the Instant Payment Flow concept. The system allows to set-up business money-transfer transactions with continuous flow of money depending on goods flow

Application of neural network to predict the future of stock exchange, aerospace ( ufo trajectory ), sound, image, noise, scientific or medical data. You need , by example, 100 days of a numeric data and the number of days to predict (by example 7 days).

Simulation platform for automated stock exchange trading. It delivers statistics to analyse performance on historic data and allows comparison between trading strategies, that can be coded in Java.

Diagram (JGraph) driven simulator. Bondgraphs > nonlinear differantial system > plot: implemented for economics and ecology. Network analysis: emergy propagation implemented. Would also fit for electronics, mechanics, cost, GWP, footprint analysis.

8. Mai 2005: We have discontinued SourceForge as a download place for our open source projects. You can now download these projects directly from the XLOG Technologies GmbH web site at http://www.xlog.ch/omonia.

The purpose of this program is to prove that given a finite number of Monkeys and a finite amount of time one monkey will be able to make more money off the stock market than any human being. Checkout our web site for an uncorrupted download.

A java based neural network framework. The Auratus network is built around an XML messaging system, allowing for a complete MVC design. Additionally, Auratus networks are constructed and at the node/edge level, allowing for advanced topologies.

Forasystems compound financial expertise into Java Business components. These components put together the financial requirements which are shared by most of financial companies and allow to build resilient applications.

The FreeMarket Application project has stopped development, but the ideas presented in the project are now being manifested in other software.