ELECTRICITY DEMAND FORECASTING USING A SARIMAMULTIPLICATIVE SINGLE NEURON HYBRID MODEL

The combination of SARIMA and neural network models are a common approach for forecasting nonlinear time series. While the SARIMA methodology is used to capture the linear components in the time series, artifi cial neural networks are applied to forecast the remaining nonlinearities in the shocks of...

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Bibliographic Details
Published in:Dyna (Medellín, Colombia) Colombia), 2013-01, Vol.80 (180), p.4-8
Main Authors: VELÁSQUEZ HENAO, JUAN DAVID, Rueda Mejia, Viviana Maria, CARLOS JAIME FRANCO CARDONA
Format: Article
Language:English
Online Access:Get full text
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Summary:The combination of SARIMA and neural network models are a common approach for forecasting nonlinear time series. While the SARIMA methodology is used to capture the linear components in the time series, artifi cial neural networks are applied to forecast the remaining nonlinearities in the shocks of the SARIMA model. In this paper, we propose a simple nonlinear time series forecasting model by combining the SARIMA model with a multiplicative single neuron using the same inputs as the SARIMA model. To evaluate the capacity of the new approach, the monthly electricity demand in the Colombian energy market is forecasted and compared with the SARIMA and multiplicative single neuron models.
ISSN:0012-7353
2346-2183