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

TL;DR

This paper presents a novel stochastic diffusion model for predicting natural gas-based electricity production in the US, utilizing trend analysis and maximum likelihood estimation to achieve accurate medium-term forecasts.

Contribution

It introduces a new stochastic diffusion process specifically designed for modeling and forecasting natural gas electricity production, with a focus on trend function analysis and ML parameter estimation.

Findings

Model effectively fits historical data

Provides reliable medium-term forecasts for 2022-2023

Demonstrates the utility of the new stochastic process

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.