Advanced Multivariate Time Series Modelling On complex environments.

Introduction

Using network chart to retrieve stored information from its local network to do predictions. All complex environments emit thermodynamic energy signals during interactions. By slicing these signals with time into single plane movements, they become the commonly termed random number sequences. Our research team investigates how non-linear analogue algorithms can be used to simulate predictive dynamics.

Symmetry is innate expressions of naturally occurred complex systems. We are able to design geometric modelling to allow symmetrical expressions to germinate, predictive regime can then be construed to execute forecasting tasks bypassing the need of big data.

Our brains are perfect examples of closed complex system environments in constant interactions with the surroundings. Thermodynamic energy dissipates in heat, electrical signals and in thoughts. Our thought streams have to be transformed into analogue information in linear progressions in order to carry out as actions. By converting thoughts into integer sequences, we may be able to conduct experiments using RDS geometric modelling to do predictions. Other example of natural complex system environment is vacuum space with quantum field fluctuations.

Technology that can successfully analyse complex system interactions will eventually usher us to an era of cross-domain AI machines.

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“I think the next century will be the century of complexity.” Stephen Hawking

“The great unexplored frontier is complexity…I am convinced that nations and people that master the new science of complexity will become the economic, cultural, and political superpowers of the next century.” Heinz Pagels (1939-1988)

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