GMDH Shell is a predictive modeling tool used for business forecasting and research. There are four editions of GMDH Shell designed for different domains:

  • Data Science
  • Business forecasting
  • Load forecasting
  • Financial markets

Data science

Data science version does not limit your modelling experiments in any way and in the same time requires you to understand predictive modeling more than other GMDH Shell versions.

When a new dataset is imported GMDH Shell pops-up a list of preconfigured templates allowing you to choose between Regression, Classification and Time Series Forecasting tasks. The following examples should help identify your modeling task:

Notable features of GMDH Shell

Solving modeling problems:
  • Multivariate time series forecasting
  • Regression (continuous value prediction)
  • Classification (prediction of a category)
  • Ranking and selection of variables
  • Polynomial curve fitting
Modeling simulation outputs the following results:
  • A set of models that can be exported to Excel
  • Predictions
  • Importance of input variables
  • Analysis of out-of-sample model accuracy
Predictive modeling work-flow:
  • Create a model
  • Save the model
  • Export the model's formula to Excel (deploy a model)
  • Load a model from a save-file
  • Apply the model to unknown instances within the analyzed file
  • Apply the model to a new data-file (scoring)
Learning algorithms:
  • GMDH-type neural networks
  • Combinatorial GMDH
Embedded data exploration:
  • File preview
  • Descriptive statistics
  • Line charts
  • Bar charts
  • Scatter plot
  • Histogram
  • Autocorrelation chart
  • Pair-wise correlations with ranking
  • Contour plot
  • Heat map
  • 3D surface
Data-file formats:
  • CSV (and any other text files with delimiters)
  • XLSX
  • XLS
  • File sets with the same extension
Data pre-processing:
  • Visual handling of input and output (target) variables and data transformations
  • Handling of missing values
  • Converting categorical (text) data into numeric values (encoding and binary decomposition)
  • Weighting of dataset rows (handling of imbalanced classification problems)
  • Time series preprocessing (lags, differences, moving average, incremental weighting of dataset rows)
  • Elementary functions (logarithmic transformation, normalization, etc.)
Dynamic post-processing
  • Average of top-ranked models
  • Quantization of predictions
  • Background execution mode via the command line
  • Dataset examples and project templates
  • One-click result recalculation for dynamically updated data files
  • Support for multi-core processors
  • Support for clustered Linux systems (Enterprise edition)
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