Neural estimation and embedology for time-series prediction
Robert E. Garza, James A. Stewart, Steven K. Rogers, Dennis W. Quinn, James R. Stright · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Time series prediction has widespread application, ranging from predicting the stock market to trying to predict future locations of scud missiles. The embedology theorem sets forth the procedures for state space manipulation and reconstruction for time series prediction. This includes embedding the time series into a higher dimensional space in order to form an attractor, a structure defined by the embedded vectors. In this paper, embedology is combined with neural technologies in an effort to create a more accurate prediction algorithm. The algorithms tested are embedology, neural networks, and Euclidean space nearest neighbors. Local linear training methods are compared to the use of the nearest neighbors as the training set for a neural network. The results of these experiments determine that the neural algorithms have the best prediction accuracies. The performance of the nearest neighbor trained neural network validates the applicability of the local linear training set.