INTERPOLATION OF MISSING DATA USING NONLINEAR AND CHAOTIC SYSTEM-ANALYSIS

A new method for interpolating missing data in a time series is introd uced. The method uses local flow approximation for interpolation and i s based on a suitable modification of some recent developments in pred icting nonlinear and chaotic time series. The method has been tested f or various geophysical and climatic time series. We find that in many cases the method gives better results than the cubic spline. The real power of this method is seen when there are several consecutive missin g values in the series.