Analysis of Residual Autocorrelation in Forecasting Energy Consumption through a Java Program

Abstract:

The study of forecasting of energy in Brazil is important for future planning, as the country has experienced crises of energy supply. And a model developed in java is an affordable and efficient tool to be used both in Brazil and in other countries. Time series analysis is highly important in many different application areas, for it allows description and modeling of a variable of interest’s behavior, thus enabling the forecasting of its future values, which serves as support for decision making. When the data used in regression analysis comprises time series, the dependency between the observations grants a dynamic quality to the regression model. In this situation, it is common to come across a problem known as residual autocorrelation, which invalidates the assumptions made about the term of error in the classical linear regression models. This paper presents a program created in Java by implementing the method of Cochrane-Orcutt for the correction of residual autocorrelation. And the application is made in the Brazilian energy final consumption forecasting.

• Periodical: Advanced Materials Research (Volumes 962-965)
• Pages: 1753-1756
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.962-965.1753
• Online since: June 2014
• Authors: Renan de Oliveira Silva, Eliane da Silva Christo*, Kelly Alonso Costa
• Keywords: Dynamic Regression Models, Energy, Java, Residual Autocorrelation

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