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Forecasting market movements using the Bayesian classification and indicators based on Singular Spectrum Analysis

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by , 07-13-2017 at 03:27 PM (4364 Views)
      
   
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One promising way to achieve this is building a recommendatory system for time-efficient trading by combining the capabilities of forecasting with the singular spectrum analysis (SSA) and important machine learning method on the basis of Bayes' Theorem. The value of the selected approach lies in that the processing of data is based on the statistical analysis methods exclusively, and does not imply groundless assumptions. This gives a clear idea of both the capabilities and limitations of the method, its perspectives in creating an automated trading system.

During the development of this system, the focus was on the scale of the time frame units from 5 minutes to an hour. A fundamentally larger scale, hours and days, is more popular in the majority of descriptions of theoretically successful statistical methods (due to the reduced contribution of the chaotic component). However, such methods are of little use in the actual practice of individual speculative trading.
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