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Program Features |
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Data Import and Export
| · | ANSI Data Import and Export. |
| · | Binary Data Import and Export. |
| · | Copy and Paste. |
| · | Drag and Drop. |
Data Editing, Formatting and Printing
| · | Automated data population. |
| · | Complete functionality for editing, formatting, and printing. |
| · | Mixed data type support: Date, Text, Number, and Formula. |
| · | Find and Replace Operations. |
| · | Cut, Copy, and Paste. |
| · | Drag and Drop. |
| · | Sorting. |
Charting
| · | Multiple chart types: Line, Bar, Point, Surface, Contour, and so forth. |
| · | One-, two-, three-, and four-dimensional charts. |
| · | Conversion among compatible chart types. |
| · | Chart Animation. |
| · | Chart formatting, printing, saving and exporting. |
Data Model Definition
| · | Easy model definition and specification. |
| · | Automatic embedding. |
| · | Automatic assignment of values for dates and text. |
| · | Unlimited number of concurrent model solutions. |
| · | Multivariate models of up to 8,192 vectors of 1,024 elements and 4,194,303 instances. |
Math Operations Between Series
| · | Addition |
| · | Subtraction |
| · | Multiplication |
| · | Division |
| · | Logical AND |
| · | Logical OR |
| · | Logical XOR |
Analysis Tools
| · | One- and two-factor Analysis of Variance. |
| · | Auto and cross Average Mutual Information. |
| · | Auto and cross Covariance and Correlation Functions. |
| · | Chi-Square Test for Population Variances. |
| · | Descriptive Statistics: Mean, Median, Mode, Geometric Mean, Harmonic Mean, Mean Deviation, Root Mean Square, Variance, Standard Deviation, Sample Variance, Sample Standard Deviation, Standard Error, Skewness, Standard Error of Skewness, Kurtosis, Standard Error of Kurtosis, Count, Sum, Range, Minimum, Maximum, Confidence Interval, and Additional Modes. |
| · | False Nearest Neighbors. |
| · | Frequency Domain Correlation. |
| · | Generalized Dimensions under different numerical methods: Ellner, Grassberger-Procaccia, Takens-Theiler, and so forth. |
| · | Histograms: Natural, Uniformly Binned, and Gaussian-Binned. |
| · | IID Tests: Box-Pierce, Difference-sign, Ljung-Box, McLeod-Li, Rank, and Turning Point. |
| · | Maximal Lyapunov Exponent: Kantz and Rosenstein methods. |
| · | One-Sample Tests for Means. |
| · | Poincare Surface of Section. |
| · | Power Spectrum Estimation: Periodogram, Averaged, Windowed, and Maximum Entropy. |
| · | Recurrence Analysis. |
| · | Running Statistics: Progressive or windowed for Mean, Root Mean Square, Variance, Mean Deviation, Standard Error, and Standard Deviation. |
| · | Simultaneous Solution of Linear Equations. |
| · | Space Time Separation Plot. |
| · | State Space Visualization (Phase Portraits) in up to 4-dimensions. |
| · | Two-sample F-Test for Variances. |
| · | Two-sample Tests for Means. |
Operation Tools
| · | User-defined functions (with functional equation parser) which can be applied directly on the data and can be used as neural threshold functions in forecast solutions. |
| · | Window Functions: Barlett, Blackman, Blackman-Harris, Dolph-Chebyshev, Half-Cycle Sine, Hamming, Hann, Kaiser, Parzen, and Welch. |
| · | Difference and Summation of series. |
| · | Digital Filter Design: Sinc Function, Remez Exchange, and user-defined Frequency Custom. |
| · | Embedding. |
| · | Event Times and Times Event. |
| · | Exponential Smoothing. |
| · | Mixed Radix Real and Complex, Forward and Inverse Fourier Transforms for one or two dimensions. |
| · | Frequency Domain Convolution. |
| · | Numerical Interpolation and Resampling: Methods for one- and multi-dimensional uniformly and arbitrarily spaced data |
| · | Moving Average. |
| · | Data Normalization: Zero Mean One-Standard Deviation with optional scaling. |
| · | Numerical Differentiation of empirical data: Methods for uniformly and arbitrarily spaced data. |
| · | Numerical Integration of empirical data: Methods for uniformly and arbitrarily spaced data. |
| · | Polynomial Expansion. |
| · | Automated data population. |
| · | Random Number Generation in several distributions: Uniform, Beta, Binomial, Chi-Square, Exponential, F-Distribution, Gamma, Gaussian, and t-Distribution, among others. |
| · | Data Sampling (see Random Number Generation for possible sampling distributions). |
| · | Savitzky-Golay for uniform and arbitrarily spaced data. |
| · | Data Scaling. |
| · | Surrogate Data Generation: Random Shuffle, Phase-Randomized, Gaussian Scaled, Fourier Shuffled, Iterated Amplitude Adjusted, and Multi-Dimensional Fourier Transformed. |
Table and Additional Data Operations
| · | Forced Text removal. |
| · | Joining and splitting information. |
| · | Row and column order reversal. |
| · | Transposition. |
Model Approximation Methods
| · | Static Global Least Squares: Solution is based on a static data section. |
| · | Dynamic Global Least Squares: Solution data section changes dynamically according to the prediction point. |
| · | Global Multilayer Perceptron: User Defined Feedforward Artificial Neural Network. |
| · | Averaged K-Nearest Neighbors. |
| · | Weighted K-Nearest Neighbors. |
| · | Local Least Squares. |
| · | Editing of Solutions. |
| · | Interactive Tests and Simulation. |
| · | Test Reports. |
| · | Residual Analysis and Confidence Estimation. |
| · | Run Solutions against external data. |
| · | Formatted account of test results. |
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