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An Enhanced Algorithm of RNN Using Trend in Time-Series

1
DU College, Daegu University, Kyungsan 38453, Korea
2
Department of Liberal Arts, Hongik University, Sejong 04066, Korea
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(7), 912; https://doi.org/10.3390/sym11070912
Received: 29 May 2019 / Revised: 27 June 2019 / Accepted: 10 July 2019 / Published: 12 July 2019
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Abstract

The concept of trend in data and a novel neural network method for the forecasting of upcoming time-series data are proposed in this paper. The proposed method extracts two data sets—the trend and the remainder—resulting in two separate learning sets for training. This method works sufficiently, even when only using a simple recurrent neural network (RNN). The proposed scheme is demonstrated to achieve better performance in selected real-life examples, compared to other averaging-based statistical forecast methods and other recurrent methods, such as long short-term memory (LSTM). View Full-Text
Keywords: time series; trend; machine learning; RNN; LSTM time series; trend; machine learning; RNN; LSTM
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Yi, D.; Bu, S.; Kim, I. An Enhanced Algorithm of RNN Using Trend in Time-Series. Symmetry 2019, 11, 912.

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