Nonlinear time series methods for analyzing behavioural sequences - Kuznetsov, N.; Bonnette, S.; Riley, M. A. (Complex systems in Sport, 2014)
This chapter provides an overview of nonlinear analysis methods that quantify the time-dependent characteristics of behavioural sequences. We review the fundamental notions useful for an understanding of these analyses and briefly summarize several frequently used methods. We then describe sample entropy (SampEn) in more detail and provide a tutorial example of calculating it. Finally, some relevant factors for the interpretation and design of experiments employing sample entropy are discussed. The analyses discussed in this chapter quantify the structure of variability in time series. While the utility of using a time series approach is determined by the motivating theoretical questions of a field of study, practically any observable phenomenon can be recorded as a time series. Many disciplines use time series to gain insights into their phenomena of interest. Stock price fluctuations have for a long time puzzled economists. In psychiatry and psychology, variations in mood over time can provide useful clinical insights into mood disorders and time series methods have led to a conceptualization of self-perception as an emergent property. Similar analyses provide clues about the structure of the cognitive system by quantifying response time variability. Perceptual-motor control has also benefited from the application of time series analyses. We believe that the methods described in this chapter provide similar insights for sport science.