Financial time series modeling should incorporate cyclical analysis. The basis of all cycles is vibration. If an object or a system is stationary, in a particular reference frame, then presumably there is no vibration; although the potential for vibration does exist. If at some point, a polarity manefests, the stationary system aquires the potential for motion. The nature of the polarity determines (to some extent) the motion’s frequency, or vibratory rate.

I conjecture, a primary constituent requisite for a polarity to arise, and thus vibration, are numbers. The most stable and fundamental of all numbers are the primes. All others are composite.

Understanding how certain numerical relationships between the primes, as well as others, might induce polarities which facilitate vibratory cycles, can be a component of both linear and non-linear cycle period determination. In general, financial market modeling can benefit from such analysis.

During my next webinar this coming Saturday (10/05/13),  I’ll indicate how certain numerical analysis, could be a theoretical component, central to cycle period determination, and thereby improve market modeling.

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