Neural estimation and embedology for time-series prediction

Robert E. Garza; James A. Stewart; Steven K. Rogers; Dennis W. Quinn; James R. Stright

doi:10.1117/12.205170

6 April 1995 Neural estimation and embedology for time-series prediction

Robert E. Garza, James A. Stewart, Steven K. Rogers, Dennis W. Quinn, James R. Stright

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205170
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

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.

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Robert E. Garza, James A. Stewart, Steven K. Rogers, Dennis W. Quinn, and James R. Stright "Neural estimation and embedology for time-series prediction", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205170

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KEYWORDS

Neural networks

Evolutionary algorithms

Reconstruction algorithms

Algorithm development

Error analysis

Data modeling

Stochastic processes

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