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Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.
Some recent advances in forecasting and control
George EP Box and Gwilym M Jenkins · 1968
Earlier work this paper cites.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
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Forecasting with artificial neural networks:: The state of the art
Guoqiang Zhang, B Eddy Patuwo, and Michael Y Hu · 1998
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Earlier work this paper cites.
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Adam: A method for stochastic optimization
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Earlier work this paper cites.
Time series analysis: forecasting and control
George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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