A Hybrid Forecasting Structure Based on Arima and Artificial Neural Network Models

A hybrid forecasting model with two subcomponents is presented in this study. A basic and secondary models are combined in a hierarchy to improve forecasting performance. This is particularly important for complex data sets with multiple patterns. Such data sets do not respond well to simple models,...

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Bibliographic Details
Published in:Applied sciences 2024-08, Vol.14 (16), p.7122
Main Authors: Atesongun, Adil, Gulsen, Mehmet
Format: Article
Language:English
Subjects:
Datasets
Energy consumption
forecast error classification
Forecasting
hybrid forecast
Hybridization
integrated forecast
Literature reviews
Methods
multi-pattern data forecasting
Neural networks
Time series
Variables
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Summary:A hybrid forecasting model with two subcomponents is presented in this study. A basic and secondary models are combined in a hierarchy to improve forecasting performance. This is particularly important for complex data sets with multiple patterns. Such data sets do not respond well to simple models, and hybrid models based on the integration of various forecasting tools often lead to better forecasting performance. This study involves the development of a hybrid forecasting framework that integrates two different models in a framework to improve prediction capability. Although the concept of hybridization is not a new issue in forecasting, our approach presents a new structure that combines two standard simple forecasting models uniquely for superior performance. Hybridization is significant for complex data sets with multiple patterns. Such data sets do not respond well to simple models, and hybrid models based on the integration of various forecasting tools often lead to better forecasting performance. The proposed architecture includes serially connected ARIMA and ANN models. The original data set is first processed by ARIMA. The error (i.e., residuals) of the ARIMA is sent to the ANN for secondary processing. Between these two models, there is a classification mechanism where the raw output of the ARIMA is categorized into three groups before it is sent to the secondary model. The algorithm is tested on well-known forecasting cases from the literature. The proposed model performs better than existing methods in most cases.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14167122