
Growing collection of advanced forecasting algorithms. Currently implemented:
ARIMA with automatic best parameter set search and smart seasonal trend detection.
Stepwise multivariate regression with the selection of the best regression subset over the whole bar history.
Proven backhistory. Model is recalculated on each historical time step using only those data which were available prior to actual historical trade date, so you see performance exactly same as you would observe on real trades.
Full intraday capable solutions available on the synchronous with datastream basis.
Realtime speed in model training. Models have accuracy comparable to most advanced neural network solvers while retaining the training speed allowing recalculate on the fly.
Ready to use professional style Expert Advisors, Indicators and System Testers.
Open scripts for for writing your own trading strategies using direct algorithmic calls.
Full automated installer integrated with Equis Metastock® versions 7 and 8. Just plugandplay.
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Freeware for fast development and application of classification type networks including the multilayer perceptron (MLP), functional link network, piecewise linear network, nearest neighbor classifier (NNC), self organizing map (SOM) and KMeans clustering. C source code for applying trained networks is provided, so users can use networks in their own applications. Usersupplied txtformat training data files, containing rows of numbers, can be of any size. Example training data is also provided. Fast VB Graphics for network classification error and SOM cluster formation are included. Extensive help files are provided in the software.
Although principally aimed at scientists and engineers, Nuclass7 is highly automated and requires very few parameter choices by the user. Advanced features include a fast MLP training algorithm (faster than BP and better than LM), input feature selection, pruning (elimination) of useless units (for MLP) and modules for PLN). Utilities are provided for counting patterns, deleting columns, combining files, splitting files, calculating column mean and standard deviation, and plotting column histograms. Training data can be compressed using the discrete KarhunenLoeve' transform (KLT). This freeware version of Nuclass7 limits the MLP to 10 hidden units, the PLN to 10 clusters, and the NNC to 50 clusters. The commercial version, which lacks these limitations, is also available for those who need it. The regression/approximation version of this software, called Numap7, is also available. Nuclass7 was developed by the Image Processing and Neural Networks Lab of Univ. of Texas at Arlington.
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Freeware for fast development and application of regression/approximation networks including the multilayer perceptron (MLP), functional link network, piecewise linear network, self organizing map (SOM) and KMeans clustering. C source code for applying trained networks is provided, so users can use networks in their own applications. Usersupplied txtformat training data files, containing rows of numbers, can be of any size. Example training data is also provided. Fast VB Graphics for network training error and SOM cluster formation are included. Extensive help files are provided in the software.
Although principally aimed at scientists and engineers, Numap7 is highly automated and requires very few parameter choices by the user. Advanced features include a fast MLP training algorithm (faster than BP and better than LM), input feature selection, pruning (elimination) of useless units (for MLP) and modules for PLN). Utilities are provided for counting patterns, deleting columns, combining files, splitting files, calculating column mean and standard deviation, and plotting column histograms. Training data can be compressed using the discrete KarhunenLoeve' transform (KLT). This freeware version of Numap7 limits the MLP to 10 hidden units and limits the PLN to 10 clusters. The commercial version, which lacks these limitations, is also available for those who need it. The classification (decision making) version of this software, called Nuclass7, is also available. Numap7.0 was developed by the Image Processing and Neural Networks Lab of Univ. of Texas at Arlington
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EasyNN is a neural network system for Microsoft Windows. It makes the creation of neural networks easy. It allows the user to produce multilayer neural networks from a grid or from text files. The user can produce training, validating and querying files using the facilities in EasyNN or using any editor, word processor or spreadsheet that supports text files. EasyNN can learn from training data and can self validate while learning. It can be queried from a file or interactively. EasyNN can produce spreadsheet like output and results files. All graphs and diagrams are updated during training and querying so the user can see how the neural networks are working.
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EasyNNplus is a neural network system for Microsoft Windows. It makes the creation of neural networks easy. It allows the user to produce multilayer neural networks from a grid or from text files and images. The user can produce training, validating and querying files using the facilities in EasyNNplus or using any editor, word processor or spreadsheet that supports text files. EasyNNplus can learn from training data and can self validate while learning. It can be queried from a file or interactively. EasyNNplus can produce spreadsheet like output and results files. All graphs and diagrams are updated during training and querying so the user can see how the neural networks are working.
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NeuroDiet is a neural network application that learns how the foods you eat are related to your health and fitness. All the neural network functions are managed automatically. You don't need to know anything about neural networks. All you need to do is tell NeuroDiet how your symptoms are changing and which foods you are eating. Every day NeuroDiet will produce a list of foods that may be causing problems with your health and fitness.
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This installation program includes evaluation versions of three products for neural network design and development: NeuroSolutions, NeuroSolutions for Excel and the Custom Solution Wizard. NeuroSolutions is a highly graphical neural network development tool for Windows 95/98/Me/NT/2000. This leading edge software combines a modular, iconbased network design interface with an implementation of advanced learning procedures, such as recurrent backpropagation, backpropagation through time and genetic optimization. The result is a virtually unconstrained environment for designing neural networks for research or to solve realworld problems. NeuroSolutions for Excel is a Microsoft Excel addin that can be used in conjunction with NeuroSolutions to simplify and enhance the process of getting data in and out of the network. The Custom Solution Wizard is a program that will take any neural network created with NeuroSolutions and automatically generate and compile a Dynamic Link Library (DLL) for that network, which you can then embed into your own application.
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The NeuroSolutions for MATLAB neural network toolbox is a valuable addition to MATLAB's technical computing capabilities allowing users to leverage the power of NeuroSolutions (www.neurosolutions.com) inside MATLAB and Simulink. The toolbox features 15 neural models, 5 learning algorithms and a host of useful utilities integrated in an easytouse interface, which requires ?next to no knowledge? of neural networks to begin using the product. The toolbox is also integrated with NeuroSolutions, which enables users to build custom networks in NeuroSolutions and use it inside Matlab using the NeuroSolutions for Matlab interface. The free evaluation edition of NeuroSolutions for MATLAB includes several demos that will walk you stepbystep through the product. In addition, you can experiment with all of the features of this product using your own data. See how easy it is to solve your problems using neural networks inside the MATLAB environment.
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TradingSolutions is a financial analysis software package that combines traditional technical analysis with stateoftheart artificial intelligence technologies. Use any combination of financial indicators in conjunction with advanced neural networks and genetic algorithms to create endofday or realtime trading models that are remarkably effective, especially in today's volatile markets. TradingSolutions' userfriendly interface allows you to perform complex financial forecasting without leaving you lost in the technology. Simple wizards guide you stepbystep through each task, while optional advanced panels give you the flexibility to adjust parameters behind the scenes. New to version 2.0 is a free Solution Service, which includes 10 neural network models and the endofday stock data for those models so that you can obtain the daily trading signals and track the performance in real time. This powerful trading tool allows you to 1) Download data directly from the Internet or import from a variety of other sources, 2) Get up and running fast with animated demonstrations and stepbystep tutorials, 3) Perform calculations on a single security or multiple securities at once, 4) Model optimal actions and predict future prices with exclusive timebased neural networks, 5) Implement your own functions, systems, and complete trading solutions, 6) Evaluate trading models for profit potential using historical backtesting, 7) Optimize your models for maximum profit. Please visit www.tradingsolutions.com to download a FREE evaluation copy of TradingSolutions or call 1800NDIDEAS for more information.
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Artificial Neural Networks are computational paradigms which implement simplified models of their biological counterparts, biological neural networks. Biological Neural Networks are the local assemblages of neurons and their dendrite connections that form the brain. The implementation of Neural Networks for brainlike computations like patterns recognition, decisions making, motory control and many others is made possible by the advent of large scale computers in the late 1950's. Conventional computers rely on programs that solve a problem using a predetermined series of steps, called algorithms. These programs are controlled by a single, complex central processing unit, and store information at specific locations in memory. Artificial Neural Networks use highly distributed representations and transformations that operate in parallel, have distributed control through many highly interconnected neurons, and store their information in variable strength connections called synapses ? just like a human brain. To train a neural network you must have a data set containing sample parameters which corresponding to the results. The data used for training is usually obtained using historical data in which the outcomes are known. You can also train a neural network by creating sample problems and answers. Once the training process is completed, the neural network will be able to predict answers when new inputs are processed.
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