Next Article in Journal
Efficient Hierarchical Identity-Based Encryption System for Internet of Things Infrastructure
Previous Article in Journal
Correlation Dynamics of Dipolar Bosons in 1D Triple Well Optical Lattice
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle

An Enhanced Algorithm of RNN Using Trend in Time-Series

DU College, Daegu University, Kyungsan 38453, Korea
Department of Liberal Arts, Hongik University, Sejong 04066, Korea
Author to whom correspondence should be addressed.
Symmetry 2019, 11(7), 912;
Received: 29 May 2019 / Revised: 27 June 2019 / Accepted: 10 July 2019 / Published: 12 July 2019
PDF [758 KB, uploaded 15 July 2019]


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Yi, D.; Bu, S.; Kim, I. An Enhanced Algorithm of RNN Using Trend in Time-Series. Symmetry 2019, 11, 912.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top