The set of algorithms currently implemented and available in the package are listed below. Moreover, each knot may hold the whole selfsimilar fractal network however elaborate and complex.

At present, the project supports the following:

• Input control procedures for automatic conversion of raw data into a data series.
• Monitoring and smart filtering of extreme values within data series.
• Mutual synchronization and aggregation of datasets.
• Multidimensional analysis of distributed lags.
• Simple linear regression (for instant prediction of a very short time series).
• A number of statistical tests, which detect seasonal components and estimate their characteristic times.
• Non-seasonal ARIMA models for individual forecasting of medium and long time series.
• Adaptive selection of seasonal ARIMA models.
• Multiple linear regression analysis with confidence intervals for the future responses.
• Selection of the regression model using a forward stepwise algorithm.
• Leaps and bounds algorithm for determining a number of best regression subsets from a full regression model.
• Special set of multivariate tests, which assist combining the above methods into a most effective model.

While most of the above algorithms are widely known and available, the real power of our solution proceeds from the ability to automatically merge these computational methods into a flexible and effective forecast model.