9309786 Weigend The progress in the last decade in predicting and understanding time series has been remarkable. Where once time series analysis was shaped by linear systems theory, it is now possible to recognize when an apparently complicated time series has been produced by a low- dimensional nonlinear system, characterize its essential properties, and build a model that can be used for prediction. At the opposite extreme, there is now a much richer framework for designing algorithms such as neural networks that can learn and adapt to the structure in time series that do not have a simple origin. This research addresses the following three questions: How to estimate the accuracy in time series prediction, how to deal with data sets that are noisy and chaotic, and how to characterize the system that temporal sequence by analyzing the predictive model. The tools to be developed in response to these questions combine recent advances from connectionism and dynamical systems theory. They will be evaluated on real-world data, submitted by various groups for consideration at the Time Series Prediction and Analysis Competition that was held under the auspices of the Santa Fe Institute. ***
