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- W2897331688 abstract "Multi-step time series prediction is known to suffer from increasing performance degradation the farther in the future the predictions are made. In this paper, we introduce two approaches to address this weakness in recursive and multioutput prediction models. In particular, we present a model that allows recursive prediction approaches to take into account the time-step index when making predictions. In addition, we propose a conditional generative adversarial network-based data augmentation model to improve prediction performance in multioutput models. We show on real-world time series datasets that the two methods improve on multi-step time series prediction in recursive and multi-output models, respectively." @default.
- W2897331688 created "2018-10-26" @default.
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- W2897331688 date "2018-07-01" @default.
- W2897331688 modified "2023-09-26" @default.
- W2897331688 title "Methods to Improve Multi-Step Time Series Prediction" @default.
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- W2897331688 doi "https://doi.org/10.1109/ijcnn.2018.8489402" @default.
- W2897331688 hasPublicationYear "2018" @default.
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