Analysis of the relationship between LSTM network traffic flow prediction performance and statistical characteristics of standard and nonstandard data
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DOI: 10.1002/for.2683
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- Wong, W.K. & Xia, Min & Chu, W.C., 2010. "Adaptive neural network model for time-series forecasting," European Journal of Operational Research, Elsevier, vol. 207(2), pages 807-816, December.
- Timo Teräsvirta & Marcelo C. Medeiros & Gianluigi Rech, 2006.
"Building neural network models for time series: a statistical approach,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 49-75.
- Marcelo C. Medeiros & Timo Terasvirta & Gianluigi Rech, 2002. "Building Neural Network Models for Time Series: A Statistical Approach," Textos para discussão 461, Department of Economics PUC-Rio (Brazil).
- Medeiros, Marcelo C. & Teräsvirta, Timo & Rech, Gianluigi, 2002. "Building neural network models for time series: A statistical approach," SSE/EFI Working Paper Series in Economics and Finance 508, Stockholm School of Economics.
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