Prediction and Forecasting

Prediction systems provided by the dedicated team of developers including 3 Ph. Ds. and 1 Doctor of Science scan each macro indicator, exactly non-stationary time series, for notable deviations from its previous statistical base and applying higher statistical moments such as Standard Deviation, Skewness, and Kurtosis to time series. The predictive power of project monitoring tools comes from observing many macro indicators turning up or down abruptly.

Machine Learning for prediction

Prediction systems software engineering based provide forecasting of financial time series by applying singular spectrum analysis(SSA) or synthesis of singular time series to an array.

Efficient forecasting software and associated services is a market-timing and market monitoring tool for Portfolio Managers, Risk Managers, and Traders who entirely goes deep into their work. It is designed to detect among 80-90% of early stages of market sell-offs or market rallies 3-5 days before they become well entrenched.  

Due to the inherent interconnectedness of global financial markets, we are developing sales prediction by tracking most of the capital markets both systematically and across all sizes. The product recognizes early warning signs.

Finally, the system tracks almost 400 daily traded market indicators such as stock markets’ indices of major sectors and industries. Such activity provides the user with the ability to correct his business plan. Therefore, implementation of business forecasting techniques leads to more wisely and predictable management and decision making

Prediction and Forecasting

Because of the growing competitiveness in the different global markets, especially financial, companies require creating market monitoring tool for gaining extremely high sales forecasting accuracy and identification of Risk-On/Risk-Off regimes of markets. Our company has created an advanced algorithm of forecasting based on the implementation of complex mathematical theories.

Our prediction system uses Bayesian Change Point Detection method, which is focused not on online monitoring but on retrospective comparing the analysis of independent periods of time between each change point of data. Implementation of Bayesian methodology to such tasks differs us from other forecasting systems.

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