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Product Comparison of;
Forecaster: Forecasting tool for MS Excel based on neural networks. It is targeted for Excel users who need a quick-to-learn and reliable forecasting tool embedded into familiar Excel interface.
Neuro Intelligence: Neuro Intelligence is neural network software designed to assist experts in solving real-world problems. Aimed at solution of real-world problems, Neuro Intelligence features only proven algorithms and techniques, is fast and easy-to-use
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The three neural networks-based tools are targeted at users with different goals. All designed to solve real-world problems. All share similar interface ideas and proprietary heuristics. To find out what product is right for you use the feature comparison table below. |
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| Feature | Forecaster | Neuro Intelligence |
| General | ||
| Excel add-in interface (optimized for MS Excel users) | ||
| Wizard-like interface (different modes for beginners and experts) |  |
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| Windows tabbed interface (optimized for experts) | |
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| Automatic and manual data analysis and preprocessing | |
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| Automatic selection of neural network architecture and training parameters | |
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| Online help system | |
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| Free technical support | |
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| Sample financial, business and scientific problems included | |
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| Analyze and Pre-process Your Data | ||
| Import popular ASCII file formats (CSV, TXT, PRN) | |
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| Import Excel files) | |
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| Custom date formats and file structure definition | |
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| Automatic data analysis and pre-processing | |
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| Automatic categorical values encoding | |
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| Automatic numeric values scaling | |
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| Automatic Date/Time values encoding | |
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| Manual min/max values specification for scaling | |
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| Visual representation of data anomalies | |
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| Outliers handling for numeric data (customizable outlier coefficient) | |
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| Missing values handling for numeric values (removal and 4 substitution options) | |
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| Missing values handling for categorical values (removal and 3 substitution options) | |
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| Automatic recognition of data entry errors (wrong type values) | |
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| Detailed data analysis and data preprocessing reports | |
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| Automatic dataset partition to training, validation and test sets (random or sequential) | |
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| Manual dataset partition to training, validation and test sets | |
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| Manual column type identification (numeric, categorical, date, time, text) | |
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| Accept/ignore records and columns manually | |
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| Preprocessed data representation | |
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| Binary columns for anomalies indication | |
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| Two methods of automatic lag columns insertion | |
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| Statistical information for data columns | |
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| Design Neural Network | ||
| Input feature selection (GA, stepwise, exhaustive). | |
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| Fully automated neural network design with a constructive algorithm. | ||
| Fully automated neural network design using architecture search heuristics | |
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| Manual architecture specification (for multi-layer perceptron) | |
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| Customizable heuristic architecture search method | |
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| Three heuristic methods of neural network architecture search. | |
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| Exhaustive architecture search with customizable parameters | |
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| Customizable search range and search sensitivity | |
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| Detailed statistics for each tested architecture | |
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| Network fitness criteria: AIC, Test set error, Correlation, R-squared | |
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| Graphical representation of network fitness | |
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| Time-series networks | |
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| Network visualization | |
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| Network sets | |
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| Automatic adjustment of learning rate and momentum for Back-Propagation algorithm | |
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| Training algorithms: Conjugate Gradient Descent, Levenberg-Marquardt, Quick-Propagation, Incremental and Batch Back-Propagation | |
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| Additional training algorithms: Quasi-Newton, Quasi-Newton (Limited Memory) | |
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| Activation functions: Linear, Logistic, Tanh, Softmax | |
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| Error functions: Sum-of-Squares, Cross-entropy | |
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| Classification model: Winner-takes-all, Confidence-limits (Accept/Reject levels) | |
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| Heuristics for automatic generation of stop training conditions | |
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| Generalization loss control (10 preset levels) | |
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| Retrain network to get better results | |
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| Manual stopping conditions (target error level, error improvement, correct classification rate, number of iterations) | |
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| Real-time control on training parameters (MSE, MAE, CCR, # of iterations). | |
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| Training Error Graph (network error by iteration) | |
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| Training Error Table (network error and error improvement by iteration) | |
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| Control Network Training Process | ||
| Real-time output of training parameters | |
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| Continue training with new parameters | |
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| Jog weights | |
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| Add jitter | |
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| Correlation and r-squared real-time graphs | |
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| Error improvement graph | |
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| Weights distribution graph | |
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| Error distribution graph | |
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| Input importance graph | |
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| Training log: test and validation set error for each iteration | |
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| Early-stopping on generalization loss | |
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| Retain and restore best network | |
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| Automatic network retrains and selection of the best network among retrains | |
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| Manual network retrain | |
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| Retrains statistics | |
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| Weights initialization: manual randomization range; optimized for Uniform or Gaussian distribution | |
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| Test and Analyze Performance | ||
| Actual vs Forecasted graph | |
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| Actual vs Forecasted scatter plot | |
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| Confusion matrix | |
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| Response graph | |
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| ROC curve | |
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| Actual vs Forecasted table with absolute and relative errors | |
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| Tolerance levels to quickly estimate overall forecasting quality | ||
| Input importance graph | |
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| Estimated forecasting error | |
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| Apply Network | ||
| Enter new cases manually or from the Clipboard | |
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| Load new cases from a new data file | |
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| Apply to selected records from your original dataset | |
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| Visual output representation with Response Graph | |
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| Output representation with Results Table | |
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| Confidence limits for network output | |
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| Save results in a separate file | |
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| Enjoy User Interface Extras | ||
| Detailed explanations on every step | |
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| Customizable reports (with preview and printing capabilities) | |
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| Reports export to HTML and XLS | |
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| Save/Load neural network | |
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| Two convenient methods of data selection in one interface (by range and by column) | ||
| Complete color customization for reports and graphs | ||
| Neural network auto save | ||
| Price | $249 | $399 |
| NOTE: Forecaster Excel can be bought via "Visit Developers Site" links below only. Special Still Applies If Cookies are on when following the link! | ||
| Compare Versions Here. All have a 30 Day Money Back Guarantee | ||
|---|---|---|
| Product | Cost | Buy |
| Forecaster | Click Buy Now For Price |
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| Neuro Intelligence | Click Buy Now For Price |
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