Nonlinear time series analysis deals with time-dependent data where the relationship between variables is not a straight line, allowing for more complex dynamics and patterns such as cycles, chaos, and abrupt changes. It is crucial in fields like economics, meteorology, and engineering where linear assumptions often fail to capture real-world phenomena accurately.