Nonlinear prediction and noise reduction
Julien Clinton Sprott · 2003
Abstract One of the most important applications of time-series analysis is prediction (or forecasting). This task has been highly developed over many years assuming the dynamics are governed by a linear model plus random noise. In this chapter we briefly review some linear methods and then describe predictors designed to exploit the possibility that the aperiodic fluctuations may be low-dimensional chaos.