Dynamic Factor Model and Artificial Neural Network Models: To Combine Forecasts or Combine Models?

In this chapter, we evaluate the forecasting performance of the model combination and forecast combination of the dynamic factor model (DFM) and the artificial neural networks (ANNs). For the model combination, the factors that are extracted from a large dataset are used as additional input to the ANN model that produces the factor-augmented artificial neural network (FAANN). Linear and nonlinear forecasts combining methods are used to combine the DFM and the ANN forecasts. The results of the best combining method are compared to the forecasts result of the FAANN model. The models are applied to forecast three time series variables using large South African monthly data. The out-of-sample root-mean-square error (RMSE) results show that the FAANN model yields substantial improvement over the individual and best combined forecasts from the DFM and ANN forecasting models and the autoregressive AR benchmark model. Further, the Diebold-Mariano test results also confirm the superiority of the FAANN model forecast’s performance over the AR benchmark model and the combined forecasts.

• Tipo de recurso: Book
• Fecha de publicación: 2018
• Institución: Universidad de Bogotá Jorge Tadeo Lozano
• Repositorio: Expeditio: repositorio UTadeo
• Idioma: eng
• OAI Identifier: oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/16834
• Acceso en línea: https://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks/dynamic-factor-model-and-artificial-neural-network-models-to-combine-forecasts-or-combine-models-; http://hdl.handle.net/20.500.12010/16834
• Palabra clave: Ingeniería de software; Red neuronal artificial; Modelo de factor dinámico; Modelo de red neuronal artificial con factor aumentado
• License: Abierto (Texto Completo)