An Exponential Autoregressive Time Series Model for Complex Data

In this paper, an exponential autoregressive model for complex time series data is presented. As estimating the parameters of nonlinear model, a three-step procedure based on quantile methods proposed. This quantile-based estimation technique has benefit being more robust compared to least/absolute squares. The performance introduced evaluated by means four established goodness-of-fit criteria. practical utility novel showcased through comparative analysis involving simulation studies and real-world illustrations.