Advanced Analytics Group has released Auguri 2.1, an integrated Windows data exploration, analysis, and forecasting tool with emphasis on nonlinear dynamical methods. Its purpose is to provide the tools for the manipulation and analysis of data through the process of predictive data mining.
Auguri reads data from most sources in ANSI or binary format, as well as its own format. They are also working on the ability to comply with the Predictive Model Markup Language (PMML).It supports drag and drop, as well as copy and paste from outside sources such as spreadsheets. It maps data to worksheets, where a worksheet may consist of one or more sheets containing data, models, solutions, and reports.
Along with common statistical analysis methods such as ANOVA, test of means, and variances, Auguri also provides extensive nonlinear methods, such as generalized fractal dimensions, Poincare surface of sections, maximal Lyapunov exponent, false nearest neighbors, space-time separation plots, averaged mutual information, and phase portraits in up to four-dimensions, as well as others.
Auguri also provides tools for the analysis of signals and series in the time and frequency domains, such as power spectrum estimation, Fourier transforms, auto- and cross-covariance and correlation functions, time-evolving statistics, and simultaneous
solutions to linear equations.
In addition, Auguri includes several methods for the generation of random numbers according to a chosen distribution, for sampling data from existing populations, and for generating surrogate data, where statistical and nonlinearity tests may be additionally carried.
With Auguri, you begin by inspecting and visualizing the data. The information is plotted to get a general idea on its format and dependency; examining the plot for trends, cycles, and missing values. After that, you typically continue to prepare the data by removing observed cycles and trends, replacing missing values and outliers, and centering and, optionally, normalizing the data. A spectral analysis may be desirable to detect noise, and remove it via filtering.
With this step done, you can continue with data reduction. Here the data is searched for dependencies (correlations), discarding irrelevant information before proceeding to create a model that explains the system. At this point, data may be split into in-sample and out-of-sample sections. The in-sample part serves to find the described dependencies and create the model, the out-of-sample one, for testing the predictive power of the proposed model. If, after testing, a model is deemed unsatisfactory, we may be tempted to go back and propose a different model, repeating these steps until we are satisfied we have achieved the best performing model.
If the results of the best performing model are acceptable; that is, if the error from the tests is small enough, then you would typically consider this case solved and explained. If the results are not acceptable, then you could take the data from the data preparation step above, and start looking for deterministic chaos before giving up further attempts to properly explain the system.
Keep in mind that Auguri not only allows you to test and explain deterministic chaos, but it also allows you to follow the classical predictive data mining cycle. You can use it in order to accept or discard a classical model before continuing into the realm of nonlinear dynamics. You may even find that sometimes a system that can be explained with classical models can be better explained as chaotic, in the deterministic sense of the word.
Auguri is a simple, basic, piece of software that relies more on its function as opposed to its form. The power of Auguri is in the engine that runs the software. It uses a spreadsheet style of system, but it is not a typical spreadsheet product. Rather, it is a convenient method of presenting information. It also uses a familiar graphic user interface to ease user interaction and operation.
While Auguri is an analysis program, it is not intended to compete with SPSS or SAS, which rely on the use of the linear analysis paradigm. Instead, it should be better looked at as a complement to these programs, in that its main focus is nonlinear methods and is an additional tool to the scientist, or model analyst to take up where these other tools fall short or fail to perform. Auguri v. 2.1 runs under Windows 98, Me, NT4, 2000, XP, 2003, Vista, and 2008, runs on Mac OS X under VMWare Fusion, Parallel or VirtualBox, and runs on Linux under VirtualBox.
Auguri is available from Advanced Analytics Group under several different licenses. Academic ($54.90), Academic+ ($79.90), Professional ($549.00), and Professional+ ($799.00). There are discounts for volume purchases as well. Check out the order page for more information, or contact Mariana Perea at email@example.com for additional information.