Toward Automatic Time-Series Forecasting Using Neural Networks

By Weizhong Yan. 2012

181 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, methodology): “As from short individual series the amount of information that can be extracted is limited, more complex methods that may have smaller bias will suffer from large variance, and finally end up performing worse (Zhang et al., 1998; Yan, 2012).”
State-of-the-art in artificial neural network applications: A survey (2018, methodology): “The comparison was made based on (i) author(s)/year of publication (ii) ANN modeling (iii) ANN area of application (iii) studied contribution to human challenges (with references to Supplementary Table 2) [11, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, ”
Layered Ensemble Architecture for Time Series Forecasting (2016, methodology): “An ensemble consisting of three radial basis function (RBF) networks is proposed in [28].”
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, methodology): “From short individual series the amount of information that can be extracted is limited (Zhang et al., 1998; Yan, 2012).”
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions (2019, background): “To this end, Yan (2012) propose a new form of ANN named as the Generalized Regression Neural Network (GRNN) which is a special form of a Radial Basis Function (RBF) Network.”