A Three-Step nonparametric change point detection method using A Meta-Heuristic algorithm for analyzing inflation trends in Nigeria (2019-2023)

Abstract

CPD is a statistical technique that finds the change points in data sequences where the statistical properties of the data have shifted. This technique has valuable applications in the field of economics as well as finance, and public health. In this study a CPD methodology is developed by proposing an enhanced three-step nonparametric approach based on the existing two-step method. The proposed framework couples Kernel Conditional Density Estimation with Fourier features and machine learning techniques for the precise identification and classification of change points. Data preprocessing for smoothness and noise reduction will be included, followed by KCDE-F for conditional density estimation, and then a machine-learning classifier refines the sensitivity and specificity of the detected change points. This paper identifies critical change points in Nigerian inflation dynamics using data from 2019 to 2023. The result shows that the developed the three-step procedure for change point detection presented here is not only also capable in change point detection but also in the estimation of structural breaks in time-series data. The wide applicability of this methodology is envisioned to extend beyond economics into other domains where the need for change point detection is compelling.