{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:34:02Z","timestamp":1775694842488,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T00:00:00Z","timestamp":1562889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1E1A1A03070311"],"award-info":[{"award-number":["NRF-2017R1E1A1A03070311"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2016R1D1A1B03930734"],"award-info":[{"award-number":["2016R1D1A1B03930734"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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\u2014the trend and the remainder\u2014resulting 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).<\/jats:p>","DOI":"10.3390\/sym11070912","type":"journal-article","created":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T11:49:38Z","timestamp":1562932178000},"page":"912","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Enhanced Algorithm of RNN Using Trend in Time-Series"],"prefix":"10.3390","volume":"11","author":[{"given":"Dokkyun","family":"Yi","sequence":"first","affiliation":[{"name":"DU College, Daegu University, Kyungsan 38453, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunyoung","family":"Bu","sequence":"additional","affiliation":[{"name":"Department of Liberal Arts, Hongik University, Sejong 04066, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inmi","family":"Kim","sequence":"additional","affiliation":[{"name":"DU College, Daegu University, Kyungsan 38453, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10696","DOI":"10.1016\/j.eswa.2009.02.043","article-title":"Forecasting stock market short-term trends using a neuro-fuzzy based methodology","volume":"36","author":"Atsalakis","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_2","unstructured":"Box, G., Jenkins, G., and Reinsel, G. (1994). Time Series Analysis: Forecasting and Control, Prentice Hall."},{"key":"ref_3","first-page":"86","article-title":"Time series prediction by estimating markov probabilities through topology preserving maps","volume":"Volume 3812","author":"Dangelmayr","year":"1999","journal-title":"Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1007\/s00521-014-1678-x","article-title":"Hybrid imperialist competitive algorithm, variable neighborhood search, and simulated annealing for dynamic facility layout problem","volume":"25","author":"Hosseini","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jin, Z., Zhou, G., Gao, D., and Zhang, Y. (2018). EEG classification using sparse Bayesian extreme learning machine for brain\u2014Computer interface. Neural Comput. Appl., 1\u20139.","DOI":"10.1007\/s00521-018-3735-3"},{"key":"ref_7","unstructured":"Keogh, E.J. (2006, January 12\u201315). A decade of progress in indexing and mining large time series databases. Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, Korea."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1007\/s00521-014-1752-4","article-title":"Fuzzy adaptive imperialist competitive algorithm for global optimization","volume":"26","author":"Khaled","year":"2015","journal-title":"Neural Comput. Appl."},{"key":"ref_9","unstructured":"Zhang, X., Yao, L., Wang, X., Monaghan, J., Mcalpine, D., and Zhang, Y. (2019). A Survey on Deep Learning based Brain Computer Interface: Recent Advances and New Frontiers. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.eswa.2017.12.015","article-title":"Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces","volume":"96","author":"Zhang","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cognit. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mueen, A., and Keogh, E. (2010, January 25\u201328). Online discovery and maintenance of time series motifs. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","DOI":"10.1145\/1835804.1835941"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1080\/09540098908915650","article-title":"A Local Learning Algorithm for Dynamic Feedforward and Recurrent Networks","volume":"1","author":"Schmidhuber","year":"1989","journal-title":"Connect. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/0893-6080(88)90007-X","article-title":"Generalization of backpropagation with application to a recurrent gas market model","volume":"1","author":"Werbos","year":"1988","journal-title":"Neural Netw."},{"key":"ref_15","first-page":"115","article-title":"Learning Precise Timing with LSTM Recurrent Networks","volume":"3","author":"Gers","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_18","unstructured":"Pascanu, R., Mikolov, T., and Bengio, Y. (2013, January 16\u201321). On the difficulty of training recurrent neural networks. Proceedings of the 30th International Conference on Machine Learning (ICML 2013), Atlanta, GA, USA."},{"key":"ref_19","unstructured":"Touretzky, D.S. (1990). The moving targets training algorithm. Advances in Neural Information Processing Systems 2, Morgan Kaufmann."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1162\/neco.1992.4.2.243","article-title":"A Fixed Size Storage O(n3) Time Complexity Learning Algorithm for Fully Recurrent Continually Running Networks","volume":"4","author":"Schmidhuber","year":"1992","journal-title":"Neural Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, X., and Ren, W. (2019). A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction. Symmetry, 11.","DOI":"10.3390\/sym11050610"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Afolabi, D., Guan, S., Man, K.L., Wong, P.W.H., and Zhao, X. (2017). Hierarchical Meta-Learning in Time Series Forecasting for Improved Inference-Less Machine Learning. Symmetry, 9.","DOI":"10.3390\/sym9110283"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, T., Guo, T., and Aberer, K. (2017, January 19\u201325). Hybrid Neural Networks for Learning the Trend in Time Series. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI- 17), Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/316"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, P., Wang, H., and Wang, W. (2011, January 12\u201316). Finding semantics in time series. Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, Athens, Greece.","DOI":"10.1145\/1989323.1989364"},{"key":"ref_26","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference for Learning Representations (ICLR 2015), San Diego, CA, USA."},{"key":"ref_27","unstructured":"Brown, R.G. (1962). Smoothing, Forecasting and Prediction, Prentice Hall."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Efron, B., and Tibshirani, R. (1993). An Introduction to the Bootstrap, Chapman & Hall\/CRC.","DOI":"10.1007\/978-1-4899-4541-9"},{"key":"ref_29","unstructured":"Prajakta, S.K. (2004). Time Series Forecasting Using Holt-Winters Exponential Smoothing, Kanwal Rekhi School of Information Technology."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cho, K., Merrienboer, B.V., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cho, K., Merrienboer, B.V., Bahdanau, D., and Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. arXiv.","DOI":"10.3115\/v1\/W14-4012"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/7\/912\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:05:09Z","timestamp":1760187909000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/7\/912"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,12]]},"references-count":31,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["sym11070912"],"URL":"https:\/\/doi.org\/10.3390\/sym11070912","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,12]]}}}