Using Particular Time Series Algorithms to Model Natural Gas Indicators for the US
Using Particular Time Series Algorithms to Model Natural Gas Indicators for the US

Author(s): Ion-Florin Raducu, Stelian Stancu
Subject(s): Social Sciences
Published by: Udruženje ekonomista i menadžera Balkana
Keywords: Seasonal autoregressive integrated moving average; Time series; Natural gas
Summary/Abstract: Natural gas is a gaseous material that primarily consists of hydrocarbons. This is a primary component of energy resources and a fossil fuel. Its primary attribute is that it produces energy with great efficiency while generating negligible amounts of pollution. Natural gas is used in a wide range of industries. It is widely used as fuel for automobiles, to produce electricity, and for home heating. Because of its benefits, which include lower carbon emissions than other energy sources, natural gas is a desirable choice for both consumers and businesses. The purpose of this paper is to use the SARIMA (Seasonal Autoregressive Integrated Moving Average) model and the additive Holt-Winters model to forecast changes in natural gas prices in the United States and compare both models. These are a few of the most popular models for forecasting and time-series analysis.

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