FORECASTING WITH ARTIFICIAL NEURAL NETWORKS: THE STATE OF THE ART

By Guoqiang Zhang, B. Patuwo, M. Hu. 1998

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Citations in the corpus

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks (2017, background): “2, is based on an autoregressive recurrent network architecture [9, 20].”
Conditional Time Series Forecasting with Convolutional Neural Networks (2017, neutral): “specifying a particular model form in advance, see [28] or [4].”
Groundwater level forecasting using artificial neural networks (2005, unknown): “Recurrent networks are the state of the art in nonlinear time series prediction, system identification, and temporal pattern classification (Zhang et al., 1998).”
GluonTS: Probabilistic Time Series Models in Python (2019, result): “This led to mixed results compared with other local modeling approaches (Zhang et al., 1998).”
Forecasting Nonlinear Crude Oil Futures Prices (2006, unknown): “…rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast values of the data [Swanson and White (1997), Zhang (1998), and Moshiri and Brown (2004).]”
Forecasting Big Time Series: Old and New (2018, background): “In the 90s, Feedforward NNs were popular among forecasters [42] with applications in electrical load [28, 18], financial time series [7], and others [9].”
Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization (2013, background): “Neural networks (NN) are an emergent technology with an increasing number of real-world applications including operational research (Lisboa and Vellido (2000) and Zhang et. al.”
Forecasting Foreign Exchange Rates With Artificial Neural Networks: A Review (2004, unknown): “Finally, ANNs are nonlinear.(59) The idea of using ANNs for forecasting exchange rates is not new.”
Avoiding Pitfalls in Neural Network Research (2007, background): “For this and other reasons [132], it is recommended that multiple output nodes be used for multistep forecasting situations .”