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• W2002158657 abstract "Neural networks have been widely applied to time series prediction over past few decades. Generally, applications of them restrict to causal models where current values are dependent on past values. In contrast, a non-causal neural network is proposed in this paper to deal with time series prediction by allowing dependence on future values. Both past and future values are used together for training and prediction. In prediction, future values are the expected values of training samples. In addition, weightings of the past and future values are incorporated into the network to improve prediction performance. Experimental results on benchmark and FX time series show that the proposed network is effective." @default.
• W2002158657 created "2016-06-24" @default.
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• W2002158657 date "2014-03-01" @default.
• W2002158657 modified "2023-10-14" @default.
• W2002158657 title "Time series prediction with a non-causal neural network" @default.
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• W2002158657 doi "https://doi.org/10.1109/cifer.2014.6924050" @default.
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