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Investigation of the behaviour and complexity of hard-to-measure parameter time series is not explored to a greater extent before modelling a wastewater treatment facility (WWTF). In this context, A dynamic non-linear chaotic approach, namely the False nearest neighbour (FNN) algorithm, is employed for the first time to investigate the influent and effluent quality parameters of a WWTF. The primary objective of this research is to analyze the parameters of a WWTF located in India for its behaviour and complexity using the FNN algorithm. The autocorrelation function and average mutual information time lags are used as the delay time (τ) in the algorithm for phase space reconstruction and further FNN analysis. The optimum embedding dimensions (mopt) from FNN plots indicate the complexity or number of optimum variables required to model the time series. For influent and effluent parameters, the mopt values fall within a range of 4–15 and 4–17, respectively, and the τ value influences this range. Wastewater time series behaviour differs, such as pure stochastic or chaotic or chaotic series with noise, which is highly dependent on τ. The future scope of the study involves integrating the retrieved behaviour and complexity into state-of-the-art artificial intelligence or data-driven techniques to forecast hard-to-measure parameters. [ABSTRACT FROM AUTHOR]