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Auguri
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Auguri is a statistical, data exploration, analysis and forecasting software with emphasis in nonlinear dynamical methods that provides the data manipulation tools required in predictive data mining and advanced analytics. Auguri Features 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 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 each 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 one Population Variance. 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 Additional Modes False Nearest Neighbors Frequency Domain Correlation Generalized Dimensions under different numerical methods: Ellner Grassberger-Procaccia Takens-Theiler and others Histograms: Pareto Natural Uniformly Binned Gaussian-Binned IID Tests: Box-Pierce Difference-sign Ljung-Box McLeod-Li Rank Turning Point Maximal Lyapunov Exponent: Kantz Rosenstein One-Sample Tests for Means: t-Test z-Test Poincare Surface of Section Power Spectrum Estimation: Periodogram Averaged Windowed Maximum Entropy Recurrence Analysis Running Statistics: Progressive or windowed Mean Root Mean Square Variance Mean Deviation Standard Error 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: t-Test z-Test Operation Tools User-defined functions with functional equation parser Window Functions: Barlett Blackman Blackman-Harris Dolph-Chebyshev Half-Cycle Sine Hamming Hann Kaiser Parzen Welch. Difference and Summation of series. Digital Filter Design: Sinc Function Remez Exchange Frequency Custom. Embedding Event Times and Times Event Exponential Smoothing Fourier Transforms for one or two dimensions: Mixed Radix Real and Complex Frequency Domain Convolution Numerical Interpolation and Resampling for one- and multi-dimensional uniformly and arbitrarily spaced data. Moving Average. Data Normalization: Zero Mean One-Standard Deviation with optional scaling. Numerical Differentiation for empirical data for uniformly and arbitrarily spaced data. Numerical Integration for empirical data 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 t-Distribution others. Data Sampling (see Random Number Generation for 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 Multi-Dimensional Fourier Transformed Table and Data Operations Forced Text Removal Joining and Splitting Row and Column order reversal Text Value Classification 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 Perceptrons: User Defined Feedforward Artificial Neural Networks Averaged K-Nearest Neighbors Weighted K-Nearest Neighbors Local Least Squares Local Multilayer Perceptrons Editing of Solutions Test Reports Forecast Error Analysis Interactive Tests and Simulation Simultaneous Solution Comparison Run Solutions against external data Formatted account of test results See Also
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