Non-Linear Time-Series Prediction by Systematic Data Exporation on a Massively Parallel Computer

In this paper we describe the application of massively parallel processing (MPP) to the problems involve sequences of numbers (for example, the daily closing values of the stock market, EEG patterns of brainwave activity, or, as discussed in this paper, the temporal values from set of equations of motion). Often the problem of interst is the prediction of some future value(s) in the sequence using only past values. Taking advantage of the power of an MPP supercomputer, we describe techniques to perform exploratory data analysis on time-series problems in a quick and effficient manner.