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The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series.
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S. Løkse, F. M. Bianchi, R. Jenssen, Training Echo State Networks with Regularization Through Dimensionality Reduction, Cognitive Computation (2017) 1–15ISSN 1866-9964, DOI: 10.1007/s12559-017-9450-z
2017
Closest in time.
Bianchi Filippo Maria, Livi Lorenzo, Alippi Cesare, Jenssen Robert, Multiplex visibility graphs to investigate recurrent neural network dynamics, Scientific Reports 7 (2017) 44037, DOI: http://dx.doi.org/10.1038/srep4403710.1038/srep44037
2017
Closest in time.
G. Zhang, B. E. Patuwo, M. Y. Hu, Forecasting with artificial neural networks:: The state of the art, International Journal of Forecasting 14 (1) (1998) 35 – 62, ISSN 0169-2070, DOI: http://doi.org/10.1016/S0169-2070(97)00044-7
2070
Closest in time.