A simulation study of artificial neural networks for nonlinear time-series forecasting

By G. Zhang, B. Patuwo, Michael Y. Hu. Computers & Operations Research, 2001

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A comparative study of linear and nonlinear models for aggregate retail sales forecasting (2003, background): “While hidden nodes are important to capture the nonlinear structure in the data, they are not as important as input nodes for nonlinear time series forecasting (Zhang et al., 2001).”
Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction (2015, background): “Furthermore, they are more appropriate than other statistical indices in measuring the performance of several algorithms on the same dataset, as argued in [26].”
Time series forecasting using a hybrid ARIMA and neural network model (2003, result): “For example, in the literature of time series forecasting with neural networks, most studies [34,36,37,44–47] use the ARIMA models as the benchmark to test the e5ectiveness of the ANN model with mixed results.”
Forecasting Financial Time Series using Neural Network and Fuzzy System-based Techniques (2002, result): “However, NNs as models for forecasting exchange rates, have been investigated in a number of previous studies [9,10].”
CRUDE OIL PRICE FORECASTING WITH TEI@I METHODOLOGY ∗ (2005, neutral): “, [21], [27–29]).”
Neural network ensemble operators for time series forecasting (2014, background): “NNs have been empirically shown to be able to forecast both linear (Zhang, 2001) and nonlinear (Zhang et al., 2001) time series of diﬀerent forms.”