Time-series analysis aims to investigate the temporal behavior of one of several variables x(t). Examples include the investigation of long-term records of mountain uplift , sea-level fluctuations, orbitally-induced insolation variations and their influence on the ice-age cycles, millenium-scale variations in the atmosphere-ocean system, the effect of the El Nino/Southern Oscillation on tropical rainfall and sedimentation (Fig. 5.1) and tidal influences on noble gas emissions from bore holes. The temporal pattern of a sequence of events can be random, clustered, cyclic or chaotic. Time-series analysis provides various tools with which to detect these temporal patterns. Understanding the underlying processes that produced the observed data allows us to predict future values of the variable. We use the Signal Processing and Wavelet Toolboxes, which contain all the necessary routines for time-series analysis.