Are ARIMA neural network hybrids better than single models?

Hybrid methods comprising autoregressive integrated moving average (ARIMA) and neural network models are generally favored against single neural network and single ARIMA models in the literature. The benefits of such methods appear to be substantial especially when dealing with non-stationary series: nonstationary linear component can be modeled using ARIMA and nonlinear component using neural networks. Our studies suggest that the use of a nonlinear component may degenerate the performance of such hybrids and that a simpler hybrid comprising linear AR model with a TDNN outperforms the more complex hybrid in tests on benchmark economic and financial time series.


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The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties أlow...
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Citation Formats
T. Taşkaya Temizel, “Are ARIMA neural network hybrids better than single models?,” 2005, Accessed: 00, 2021. [Online]. Available: