Nonlinear Prediction, Chaos, and Noise

We present a brief overview of some new methodologies for making predictions on time-series data. These ideas stem from two rapidly growing fields: nonlinear dynamics (chaos) theory and parallel distributed processing. Examples are presented that show the usefulness of such methods in making short-term predictions. It is suggested that such methodologies are capable of distinguishing between chaos and noise. Implications of these ideas and methods in the study of weather and climate are discussed.