PROCESSING TECHNIQUES FOR CHAOTIC TIME SERIES

RECENTLY A NEW PARADIGM FOR THE INTERPRETATION OF IRREGULAR APERIODIC TIMES SERIES HAS EMERGED, BASED ON THE DYNAMICS OF THE LOWDIMENSIONAL NONLINEAR SYSTEMS AND DISTINGUISHES BETWEEN "NOISE" AS TRADITIONALLY USED IN STATISTICS AND THE NOTION OF "CHAOS," WHICH IS RANDOMNESS GENERATED BY DETERMINISTIC EQUATIONS OF MOTION. WE PROPOSE TO INVESTIGATE THE FEASIBILITY OF METHODS TO DISTINGUISH BETWEEN THESE TWO SOURCES OF IRREGULARITY IN TIME SERIES DATA IN ORDER TO TEST NEW NONLINEAR FORECASTING TECHNIQUES. IN PARTICULAR, WE PROPOSE TO SURVEY THE FIELD OF CHAOS TECHNOLOGY WITH A VIEW TO APPLYING THESE TECHNIQUES TO THE ANALYSIS AND DISPLAY OF BROADBAND ACOUSTIC SIGNALS.