Simultaneous prediction of multiple financial time series using supervised learning and chaos theory

"Embedded" time series are often used with neural networks or other supervised learning algorithms to generate predictions. Work in chaos theory has pointed to methods of determining the optimal embedding parameters for individual time series. The hypothesis is explored that these methods also hold when multiple time series are used together to generate a prediction, and that the optima for the individual series combined are the optimum for the group. A novel prediction explanation mechanism is described. Examples will be taken from foreign exchange time series, and the analyses will be performed using The Prophet, a time series prediction program.<
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