Kromwyrm Artificial Intelligence Forecasting
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Kromwyrm A.I. Systems



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Described here is an artificial intelligence, a learning algorithm, that is used to find patterns within a time series. The time series that we analyze is a measurable quantity, a collection of data points that vary over time. It is produced at the output of an unknown, unviewable system, the black box. In this model the input like the black box is also unknown.

  black box figure  

The algorithm searches for the hidden patterns within the time series by following a set of steps that will try to replicate the unknown black box, thus, it tries to recreate the system that initially produced the output time series. Once an approximate model is found, the input is shifted into the future through the derived black box to generate a possible prediction of the system at the output.

  black box figure  

The black box consists of a feed-forward neural network.  It is trained using a back propagation algorithm, enveloped within a genetic algorithm.  A random noise generator with various distributions is employed throughout the architecture of the network.

Techniques from time series analysis and global optimization are used to help the algorithm arrive at a stable solution for the best neural network, which ultimately produces the resulting prediction.  For example, applications in time series analysis suggest that the information about the unknown black box model and its input signals are embedded in the output signal.  The algorithm's analysis of the output signal is used to solve for the unknown properties of the black box and input signal.

Problems of parroting are solved in a unique manner, allowing the neural nets to settle upon useful solutions.

The A.I. is capable of analyzing time-dependent data, such as the price of raw materials, the price of stocks, or the magnitude of any pertinent sequential data, for the purposes of forecasting future patterns.  

Below are further descriptions of the inner workings of this A.I., along with some examples of its past performance.

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