Description: Non-linear time series are datasets that exhibit patterns and behaviors that cannot be adequately described by simple linear relationships over time. Unlike linear time series, where changes in data are proportional and predictable, non-linear series can display complex behaviors, such as cycles, changing trends, and interdependent relationships that vary over time. These characteristics make the analysis of non-linear time series a challenge, as they require more sophisticated statistical methods and algorithms for modeling and prediction. Identifying non-linear patterns is crucial in various disciplines, as it allows analysts and scientists to extract valuable information from data that might otherwise appear chaotic or random. In the fields of data mining, model optimization, and statistics, the study of non-linear time series has become increasingly relevant, especially with the growth of large volumes of data and the need for accurate predictions in various contexts, such as economics, meteorology, and public health.
