Estimating and forecasting future conditions govern many critical business activities, such as inventory control, procurement of supplies, labor cost estimation, and prediction of product demand. Inaccurate or misleading estimates can result in aggravation or total company chaos. Lost sales, inefficient allocation of resources, and bloated inventories are often a direct result of forecasts that failed to predict future conditions. In the financial industry, inaccurate forecasts for stocks and other investments can result in poor trades and lost opportunities for gain. Inaccurate predictions of sporting outcomes can result in poor wagering and resulting losses.
Microsoft Excel provides a built-in tool for predictions, but the accuracy of its results is significantly reduced when non-linear relationships or missing data are present, which is often the case when analyzing historical business, investment, or sports data. Neural networks are a proven, widely used technology for such complex prediction problems. Loosely modeled after the human brain, neural networks are interconnected networks of independent processors that by changing their connections (known as training) learn the solution to a problem.
One of the main reasons analysts have been slow to use advanced methods such as neural networks to improve forecasts is that such methods can be difficult to master. NeuroXL Predictor removes the psychological and practical barriers by hiding the complexity of its advanced neural network-based methods while taking advantage of analysts' existing knowledge of Microsoft Excel spreadsheets. Since users make predictions through the familiar Excel interface, learning time is minimal, greatly reducing the interval between loading the software and performing useful predictions. The application is extremely intuitive and easy-to-use for beginners yet powerful enough for the most demanding professionals. Also, using NeuroXL Predictor requires no prior knowledge of neural networks.
In summary, these are the key advantages of NeuroXL Predictor:
Prime applications for NeuroXL Predictor include
Forecasting Stocks and other Investments: The ability of NeuroXL Predictor to discover non-linear relationships in input data makes it ideally suited for forecasting dynamic systems like the stock market. The price of stocks and other investment vehicles such as bonds, derivatives and options are also influenced by many different factors that are often interrelated. Traditional forecasting methods, such as regression and data reduction models, are limited in their effectiveness as they make assumptions about the distribution of the underlying data, and often fail to recognize the interrelatedness of variables. NeuroXL Predictor, drawing on the latest in artificial intelligence research, recognizes even subtle relationships between variables.
In addition to stock market prediction, NeuroXL Predictor is also ideally suited to making predictions in other financial areas, such as:
Sales Forecasting: Sales forecasting lets businesses make better purchasing decisions and manage inventory more efficiently. It can also identify new opportunities for increased sales. However, sales forecasting is extremely difficult, since a large number of factors influence sales that are often interrelated. Price, seasonality, advertising, and competitor behavior are all factors that influence sales. NeuroXL Predictor analyzes historical data to learn the interrelation between these factors, and makes predictions of sales levels for various combinations.
In addition to sales forecasting, NeuroXL Predictor is ideal for many other business activities, including:
Sports Predictions: NeuroXL Predictor's ability to handle multiple non-linear relationships makes it ideally suited to predicting the outcome of team sports and racing events. For example, with thoroughbred horse racing, NeuroXL Predictor can simultaneously examine the relationship between multiple contenders, resulting in more accurate predictions of winners than traditional estimation techniques. A typical horse or greyhound race involves a complex problem domain, often with 50 performance variables for 6-9 animals. NeuroXL Predictor's robust neural-network-based implementation allows it to detect relevant patterns in such data sets, resulting in high-quality predictions.
Most neural network prediction software requires the user to learn about neural networks, complete large tutorials, and/or perform data pre-processing. NeuroXL Predictor requires no prior knowledge of neural networks and hides the complexity from the user, allowing for useful predictions often just minutes after installation. Users just need to specify input and output references, perform a few mouse movements, and their prediction is returned.
NeuroXL Predictor also offers compatibility with Microsoft Excel-based trading software and with the entire AnalyzerXL product suite.
This software is not reviewed yet.
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