A new stochastic diffusion process to model and predict electricity production from natural gas sources in the United States

Abstract

This paper introduces a new stochastic diffusion process to model the electricity production from natural gas
sources (as a percentage of total electricity production) in
the United States. The method employs trend function
analysis to generate fits and forecasts with both conditional
and unconditional estimated trend functions. Parameters
are estimated using the maximum likelihood (ML) method,
based on discrete sampling paths of the variable ”electricity
production from natural gas sources in the United States”
with annual data from 1990 to 2021. The results show that
the proposed model effectively fits the data and provides
dependable medium-term forecasts for 2022-2023