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 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:

- Multivariate time series forecasting

- Regression (continuous value prediction)

- Classification (prediction of a category)

- Ranking and selection of variables

- Polynomial curve fitting

- A set of models that can be exported to Excel

- Predictions

- Importance of input variables

- Analysis of out-of-sample model accuracy

- 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)

- GMDH-type neural networks

- Combinatorial GMDH

- File preview

- Descriptive statistics

- Line charts

- Bar charts

- Scatter plot

- Histogram

- Autocorrelation chart

- Pair-wise correlations with ranking

- Contour plot

- Heat map

- 3D surface

- CSV (and any other text files with delimiters)

- XLSX

- XLS

- File sets with the same extension

- 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.)

- 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)

You are here: Introduction