{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T22:18:09Z","timestamp":1730326689296,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,1,13]]},"DOI":"10.1145\/3582177.3582187","type":"proceedings-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T18:06:00Z","timestamp":1680285960000},"page":"55-61","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Strategies of Multi-Step-ahead Forecasting for Chaotic Time Series using Autoencoder and LSTM Neural Networks: A Comparative Study"],"prefix":"10.1145","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-9152-6208","authenticated-orcid":false,"given":"Ngoc Phien","family":"Nguyen","sequence":"first","affiliation":[{"name":"Center for Applied Information Technology & Faculty of Information Technology, Ton Duc Thang University, Vietnam and \rDepartment of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, Czech Republic"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1805-9284","authenticated-orcid":false,"given":"Tuan Anh","family":"Duong","sequence":"additional","affiliation":[{"name":"Department of IT, Ho Chi Minh City University of Foreign Languages and Information Technology, Vietnam"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8481-0136","authenticated-orcid":false,"given":"Platos","family":"Jan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, VSB Technical University of Ostrava, Czech Republic"}]}],"member":"320","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"volume-title":"International Conference on Intelligent Systems and Knowledge Engineering 2007","author":"Ma L.-l.","key":"e_1_3_2_1_1_1","unstructured":"L.-l. Ma and X.-s. Xu , \" RBF Network-Based Chaotic Time Series Prediction and It's Application in Foreign Exchange Market ,\" in International Conference on Intelligent Systems and Knowledge Engineering 2007 , 2007: Atlantis Press, pp. 19-22. L.-l. Ma and X.-s. Xu, \"RBF Network-Based Chaotic Time Series Prediction and It's Application in Foreign Exchange Market,\" in International Conference on Intelligent Systems and Knowledge Engineering 2007, 2007: Atlantis Press, pp. 19-22."},{"key":"e_1_3_2_1_2_1","volume-title":"Evidence from the shanghai composite index in China,\" in2009 International Conference on Test and Measurement","author":"Zhao H.","year":"2009","unstructured":"H. Zhao , \" A chaotic time series prediction based on neural network : Evidence from the shanghai composite index in China,\" in2009 International Conference on Test and Measurement , 2009 , vol. 2: IEEE, pp. 382- 385 . H. Zhao, \"A chaotic time series prediction based on neural network: Evidence from the shanghai composite index in China,\" in2009 International Conference on Test and Measurement, 2009, vol. 2: IEEE, pp. 382-385."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2004.09.104"},{"key":"e_1_3_2_1_4_1","first-page":"679","volume-title":"Short-term passenger flow forecasting based on phase space reconstruction and LSTM,\" in International Conference on Electrical and Information Technologies for Rail Transportation","author":"Zhang Y.","year":"2017","unstructured":"Y. Zhang , J. Zhu , and J. Zhang , \" Short-term passenger flow forecasting based on phase space reconstruction and LSTM,\" in International Conference on Electrical and Information Technologies for Rail Transportation , 2017 : Springer , pp. 679 - 688 . Y. Zhang, J. Zhu, and J. Zhang, \"Short-term passenger flow forecasting based on phase space reconstruction and LSTM,\" in International Conference on Electrical and Information Technologies for Rail Transportation, 2017: Springer, pp. 679-688."},{"issue":"1","key":"e_1_3_2_1_5_1","first-page":"71","article-title":"Combined method of chaotic theory and neural networks for water quality prediction","volume":"17","author":"Zhang S.","year":"2010","unstructured":"S. Zhang , W. Li , J. Nan , G. Wang , and L. Zhao , \" Combined method of chaotic theory and neural networks for water quality prediction ,\" Journal of Northeast Agricultural University (English Edition) , vol. 17 , no. 1 , pp. 71 - 76 , 2010 . S. Zhang, W. Li, J. Nan, G. Wang, and L. Zhao, \"Combined method of chaotic theory and neural networks for water quality prediction,\" Journal of Northeast Agricultural University (English Edition), vol. 17, no. 1, pp. 71-76, 2010.","journal-title":"Journal of Northeast Agricultural University (English Edition)"},{"key":"e_1_3_2_1_6_1","first-page":"1","volume-title":"A method of flood forecasting of chaotic radial basis function neural network,\" in2010 2nd International Workshop on Intelligent Systems and Applications","author":"Xie J.-c.","year":"2010","unstructured":"J.-c. Xie , T.-p. Wang , J.-l. Zhang , and Y. Shen , \" A method of flood forecasting of chaotic radial basis function neural network,\" in2010 2nd International Workshop on Intelligent Systems and Applications , 2010 : IEEE , pp. 1 - 5 . J.-c. Xie, T.-p. Wang, J.-l. Zhang, and Y. Shen, \"A method of flood forecasting of chaotic radial basis function neural network,\" in2010 2nd International Workshop on Intelligent Systems and Applications, 2010: IEEE, pp. 1-5."},{"key":"e_1_3_2_1_7_1","first-page":"506","volume-title":"Chaotic analysis of seismic time series and short-term prediction with RBF neural networks,\" in2009 Third International Conference on Genetic and Evolutionary Computing","author":"Zhang J.","year":"2009","unstructured":"J. Zhang , Y. Chen , and Y. Wang , \" Chaotic analysis of seismic time series and short-term prediction with RBF neural networks,\" in2009 Third International Conference on Genetic and Evolutionary Computing , 2009 : IEEE , pp. 506 - 509 . J. Zhang, Y. Chen, and Y. Wang, \"Chaotic analysis of seismic time series and short-term prediction with RBF neural networks,\" in2009 Third International Conference on Genetic and Evolutionary Computing, 2009: IEEE, pp. 506-509."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-018-9914-5"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.45.3403"},{"key":"e_1_3_2_1_10_1","first-page":"366","article-title":"Detecting strange attractors in turbulence,\" in Dynamical systems and turbulence","volume":"1980","author":"Takens F.","year":"1981","unstructured":"F. Takens , \" Detecting strange attractors in turbulence,\" in Dynamical systems and turbulence , Warwick 1980 : Springer, 1981 , pp. 366 - 381 . F. Takens, \"Detecting strange attractors in turbulence,\" in Dynamical systems and turbulence, Warwick 1980: Springer, 1981, pp. 366-381.","journal-title":"Warwick"},{"key":"e_1_3_2_1_11_1","first-page":"24","volume-title":"Time series prediction using DBN and ARIMA,\" in2015 International Conference on Computer Application Technologies","author":"Hirata T.","year":"2015","unstructured":"T. Hirata , T. Kuremoto , M. Obayashi , S. Mabu , and K. Kobayashi , \" Time series prediction using DBN and ARIMA,\" in2015 International Conference on Computer Application Technologies , 2015 : IEEE , pp. 24 - 29 . T. Hirata, T. Kuremoto, M. Obayashi, S. Mabu, and K. Kobayashi, \"Time series prediction using DBN and ARIMA,\" in2015 International Conference on Computer Application Technologies, 2015: IEEE, pp. 24-29."},{"key":"e_1_3_2_1_12_1","first-page":"1130","volume-title":"Forecast chaotic time series data by DBNs,\" in2014 7th International Congress on Image and Signal Processing","author":"Kuremoto T.","year":"2014","unstructured":"T. Kuremoto , M. Obayashi , K. Kobayashi , T. Hirata , and S. Mabu , \" Forecast chaotic time series data by DBNs,\" in2014 7th International Congress on Image and Signal Processing , 2014 : IEEE , pp. 1130 - 1135 . T. Kuremoto, M. Obayashi, K. Kobayashi, T. Hirata, and S. Mabu, \"Forecast chaotic time series data by DBNs,\" in2014 7th International Congress on Image and Signal Processing, 2014: IEEE, pp. 1130-1135."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07143-2"},{"volume-title":"International Conference on System Science and Engineering (ICSSE)","author":"Yang C.-H.","key":"e_1_3_2_1_14_1","unstructured":"C.-H. Yang and H.-Y. Shen , \" Analysis and Prediction of Chaotic Time Series Based on Deep Learning Neural Networks ,\" in2020 International Conference on System Science and Engineering (ICSSE) , 2020: IEEE, pp. 1-9. C.-H. Yang and H.-Y. Shen, \"Analysis and Prediction of Chaotic Time Series Based on Deep Learning Neural Networks,\" in2020 International Conference on System Science and Engineering (ICSSE), 2020: IEEE, pp. 1-9."},{"key":"e_1_3_2_1_15_1","first-page":"157","volume-title":"A Comparison between Deep Belief Network and LSTM in Chaotic Time Series Forecasting,\" in2021 The 4th International Conference on Machine Learning and Machine Intelligence","author":"Phien N. Ngoc","year":"2021","unstructured":"N. Ngoc Phien , D. Tuan Anh , and J. Platos , \" A Comparison between Deep Belief Network and LSTM in Chaotic Time Series Forecasting,\" in2021 The 4th International Conference on Machine Learning and Machine Intelligence , 2021 , pp. 157 - 163 . N. Ngoc Phien, D. Tuan Anh, and J. Platos, \"A Comparison between Deep Belief Network and LSTM in Chaotic Time Series Forecasting,\" in2021 The 4th International Conference on Machine Learning and Machine Intelligence, 2021, pp. 157-163."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.01.039"},{"key":"e_1_3_2_1_17_1","first-page":"142","volume-title":"Comparison of strategies for multi-step-ahead prediction of time series using neural network,\" in2015 International Conference on Advanced Computing and Applications (ACOMP)","author":"An N. H.","year":"2015","unstructured":"N. H. An and D. T. Anh , \" Comparison of strategies for multi-step-ahead prediction of time series using neural network,\" in2015 International Conference on Advanced Computing and Applications (ACOMP) , 2015 : IEEE , pp. 142 - 149 . N. H. An and D. T. Anh, \"Comparison of strategies for multi-step-ahead prediction of time series using neural network,\" in2015 International Conference on Advanced Computing and Applications (ACOMP), 2015: IEEE, pp. 142-149."},{"key":"e_1_3_2_1_18_1","first-page":"337","article-title":"Strategies of Multi-Step-ahead Forecasting for Blood Glucose Level using LSTM Neural Networks: A Comparative Study","author":"Idrissi T. E.","year":"2020","unstructured":"T. E. Idrissi , A. Idri , I. Kadi , and Z. Bakkoury , \" Strategies of Multi-Step-ahead Forecasting for Blood Glucose Level using LSTM Neural Networks: A Comparative Study ,\" in HEALTHINF , 2020 , pp. 337 - 344 . T. E. Idrissi, A. Idri, I. Kadi, and Z. Bakkoury, \"Strategies of Multi-Step-ahead Forecasting for Blood Glucose Level using LSTM Neural Networks: A Comparative Study,\" in HEALTHINF, 2020, pp. 337-344.","journal-title":"HEALTHINF"},{"key":"e_1_3_2_1_19_1","volume-title":"Benchmark machine learning approaches with classical time series approaches on the blood glucose level prediction challenge,\" in KHD@ IJCAI","author":"Xie J.","year":"2018","unstructured":"J. Xie and Q. Wang , \" Benchmark machine learning approaches with classical time series approaches on the blood glucose level prediction challenge,\" in KHD@ IJCAI , 2018 . J. Xie and Q. Wang, \"Benchmark machine learning approaches with classical time series approaches on the blood glucose level prediction challenge,\" in KHD@ IJCAI, 2018."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.33.1134"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/0167-2789(94)90226-7"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0180944"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-55320-6"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6647534"},{"key":"e_1_3_2_1_26_1","volume-title":"https:\/\/colab.research.google.com, last accessed","author":"Colab Google","year":"2022","unstructured":"Google Colab , https:\/\/colab.research.google.com, last accessed 2022 . Google Colab, https:\/\/colab.research.google.com, last accessed 2022."},{"key":"e_1_3_2_1_27_1","volume-title":"http:\/\/keras.io, last accessed","author":"Cholett F.","year":"2022","unstructured":"F. Cholett , http:\/\/keras.io, last accessed 2022 . F. Cholett, http:\/\/keras.io, last accessed 2022."},{"key":"e_1_3_2_1_28_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.","author":"Kingma D.P.","year":"2014","unstructured":"D.P. Kingma , J. Ba , J. , 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. D.P. Kingma, J. Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105728"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11771-017-3554-1"}],"event":{"name":"IPMV2023: 2023 5th International Conference on Image Processing and Machine Vision","acronym":"IPMV2023","location":"Macau China"},"container-title":["Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3582177.3582187","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T20:01:03Z","timestamp":1701374463000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3582177.3582187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,13]]},"references-count":30,"alternative-id":["10.1145\/3582177.3582187","10.1145\/3582177"],"URL":"https:\/\/doi.org\/10.1145\/3582177.3582187","relation":{},"subject":[],"published":{"date-parts":[[2023,1,13]]},"assertion":[{"value":"2023-03-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}