{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T05:50:53Z","timestamp":1723355453961},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1016\/j.eswa.2022.118516","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T15:14:18Z","timestamp":1660749258000},"page":"118516","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":10,"special_numbering":"C","title":["Multi-step forecasting of multivariate time series using multi-attention collaborative network"],"prefix":"10.1016","volume":"211","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0998-0554","authenticated-orcid":false,"given":"Xiaoyu","family":"He","sequence":"first","affiliation":[]},{"given":"Suixiang","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Xiulin","family":"Geng","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Lingyu","family":"Xu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2022.118516_b1","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.neunet.2021.05.035","article-title":"IGAGCN: Information geometry and attention-based spatiotemporal graph convolutional networks for traffic flow prediction","volume":"143","author":"An","year":"2021","journal-title":"Neural Networks"},{"key":"10.1016\/j.eswa.2022.118516_b2","series-title":"3rd international conference on learning representations, ICLR 2015","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2015"},{"key":"10.1016\/j.eswa.2022.118516_b3","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.hal.2015.01.002","article-title":"Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibilities and potentials","volume":"43","author":"Chen","year":"2015","journal-title":"Harmful Algae"},{"key":"10.1016\/j.eswa.2022.118516_b4","series-title":"2018 IEEE international conference on data mining (ICDM)","first-page":"49","article-title":"TADA: Trend alignment with dual-attention multi-task recurrent neural networks for sales prediction","author":"Chen","year":"2018"},{"key":"10.1016\/j.eswa.2022.118516_b5","unstructured":"Choi, E., Bahadori, M. T., Sun, J. M., Kulas, J. A., Schuetz, A., & Stewart, W. F. (2016). RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. In Proceedings of the 30th international conference on neural information processing systems (pp. 3512\u20133520)."},{"key":"10.1016\/j.eswa.2022.118516_b6","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.neucom.2019.12.118","article-title":"Multivariate time series forecasting via attention-based encoder-decoder framework","volume":"388","author":"Du","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2022.118516_b7","doi-asserted-by":"crossref","unstructured":"Fan, C. Y., Zhang, Y. Z., Pan, Y., Li, X. Y., Zhang, C., Yuan, R., Wu, D., Wang, W. S., Pei, J., & Huang, H. (2019). Multi-horizon time series forecasting with temporal attention learning. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2527\u20132535).","DOI":"10.1145\/3292500.3330662"},{"key":"10.1016\/j.eswa.2022.118516_b8","series-title":"2019 IEEE international conference on data mining (ICDM)","first-page":"1036","article-title":"CAMP: Co-attention memory networks for diagnosis prediction in healthcare","author":"Gao","year":"2019"},{"key":"10.1016\/j.eswa.2022.118516_b9","series-title":"International conference on machine learning","first-page":"2494","article-title":"Exploring interpretable LSTM neural networks over multi-variable data","author":"Guo","year":"2019"},{"key":"10.1016\/j.eswa.2022.118516_b10","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1038\/s41586-019-1559-7","article-title":"Deep learning for multi-year ENSO forecasts","volume":"573","author":"Ham","year":"2019","journal-title":"Nature"},{"key":"10.1016\/j.eswa.2022.118516_b11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2014.05.003","article-title":"Noise model based \u03bd-support vector regression with its application to short-term wind speed forecasting","volume":"57","author":"Hu","year":"2014","journal-title":"Neural Networks"},{"key":"10.1016\/j.eswa.2022.118516_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106139","article-title":"A deep learning model to effectively capture mutation information in multivariate time series prediction","volume":"203","author":"Hu","year":"2020","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2022.118516_b13","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.neucom.2019.11.060","article-title":"Multistage attention network for multivariate time series prediction","volume":"383","author":"Hu","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2022.118516_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2019.116779","article-title":"Holt\u2013winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption","volume":"193","author":"Jiang","year":"2020","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2022.118516_b15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2019.12.030","article-title":"Transductive LSTM for time-series prediction: An application to weather forecasting","volume":"125","author":"Karevan","year":"2020","journal-title":"Neural Networks"},{"key":"10.1016\/j.eswa.2022.118516_b16","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1007\/s12065-018-00196-0","article-title":"Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network","volume":"12","author":"Ke","year":"2019","journal-title":"Evolutionary Intelligence"},{"key":"10.1016\/j.eswa.2022.118516_b17","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neucom.2013.03.047","article-title":"Time series forecasting using a deep belief network with restricted Boltzmann machines","volume":"137","author":"Kuremoto","year":"2014","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2022.118516_b18","series-title":"The 41st international ACM SIGIR conference on research& development in information retrieval","first-page":"95","article-title":"Modeling long- and short-term temporal patterns with deep neural networks","author":"Lai","year":"2018"},{"key":"10.1016\/j.eswa.2022.118516_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2019.116187","article-title":"Time-series forecasting of coal-fired power plant reheater metal temperatures using encoder-decoder recurrent neural networks","volume":"189","author":"Laubscher","year":"2019","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2022.118516_b20","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.asoc.2017.06.035","article-title":"Multivariate time series anomaly detection: A framework of hidden Markov models","volume":"60","author":"Li","year":"2017","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2022.118516_b21","series-title":"Asian conference on machine learning","first-page":"454","article-title":"Stock price prediction using attention-based multi-input LSTM","author":"Li","year":"2018"},{"key":"10.1016\/j.eswa.2022.118516_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106508","article-title":"DTDR-ALSTM: Extracting dynamic time-delays to reconstruct multivariate data for improving attention-based LSTM industrial time series prediction models","volume":"211","author":"Li","year":"2021","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2022.118516_b23","doi-asserted-by":"crossref","unstructured":"Liang, Y. X., Ke, S. Y., Zhang, J. B., Yi, X. W., & Zheng, Y. (2018). GeoMAN: Multi-level attention networks for geo-sensory time series prediction. In Proceedings of the 26th international joint conference on artificial intelligence (pp. 3428\u20133434).","DOI":"10.24963\/ijcai.2018\/476"},{"key":"10.1016\/j.eswa.2022.118516_b24","article-title":"Assessing Beijing\u2019s PM2.5 pollution: severity, weather impact, APEC and winter heating","volume":"471","author":"Liang","year":"2015","journal-title":"Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences"},{"key":"10.1016\/j.eswa.2022.118516_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.113082","article-title":"DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction","volume":"143","author":"Liu","year":"2020","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2022.118516_b26","series-title":"IJCAI","first-page":"3180","article-title":"Dyat nets: Dynamic attention networks for state forecasting in cyber-physical systems","author":"Muralidhar","year":"2019"},{"key":"10.1016\/j.eswa.2022.118516_b27","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.knosys.2017.03.027","article-title":"Red tide time series forecasting by combining ARIMA and deep belief network","volume":"125","author":"Qin","year":"2017","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2022.118516_b28","doi-asserted-by":"crossref","unstructured":"Qin, Y., Song, D. J., Chen, H. F., Cheng, W., Jiang, G. F., & Cottrell, G. (2017). A dual-stage attention-based recurrent neural network for time series prediction. In Proceedings of the 26th international joint conference on artificial intelligence (pp. 2627\u20132633).","DOI":"10.24963\/ijcai.2017\/366"},{"issue":"1984","key":"10.1016\/j.eswa.2022.118516_b29","article-title":"Gaussian processes for time-series modelling","volume":"371","author":"Roberts","year":"2013","journal-title":"Philosophical Transactions of the Royal Society of London A (Mathematical and Physical Sciences)"},{"key":"10.1016\/j.eswa.2022.118516_b30","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","article-title":"Time series forecasting of petroleum production using deep LSTM recurrent networks","volume":"323","author":"Sagheer","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2022.118516_b31","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1007\/s12205-018-0128-1","article-title":"Application of support vector regression for modeling low flow time series","volume":"23","author":"Sahoo","year":"2019","journal-title":"KSCE Journal of Civil Engineering"},{"key":"10.1016\/j.eswa.2022.118516_b32","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1007\/s10994-019-05815-0","article-title":"Temporal pattern attention for multivariate time series forecasting","volume":"108","author":"Shih","year":"2019","journal-title":"Machine Learning"},{"key":"10.1016\/j.eswa.2022.118516_b33","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.3390\/w12061822","article-title":"Prediction of chlorophyll-a concentrations in the nakdong river using machine learning methods","volume":"12","author":"Shin","year":"2020","journal-title":"Water"},{"key":"10.1016\/j.eswa.2022.118516_b34","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TNNLS.2015.2411629","article-title":"A bias and variance analysis for multistep-ahead time series forecasting","volume":"27","author":"Taieb","year":"2016","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2022.118516_b35","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2022.118516_b36","series-title":"Advances in neural information processing systems","first-page":"2692","article-title":"Pointer networks","author":"Vinyals","year":"2015"},{"key":"10.1016\/j.eswa.2022.118516_b37","series-title":"Statistical yearbook of CHINA-ASEAN 2018","first-page":"244","author":"Wang","year":"2018"},{"key":"10.1016\/j.eswa.2022.118516_b38","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.neucom.2018.12.027","article-title":"A hierarchical attention model for rating prediction by leveraging user and product reviews","volume":"332","author":"Xing","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2022.118516_b39","series-title":"International conference on machine learning","first-page":"2048","article-title":"Show, attend and tell: Neural image caption generation with visual attention","author":"Xu","year":"2015"},{"key":"10.1016\/j.eswa.2022.118516_b40","series-title":"IJCAI","first-page":"5349","article-title":"Knowledge-enhanced hierarchical attention for community question answering with multi-task and adaptive learning","author":"Yang","year":"2019"},{"key":"10.1016\/j.eswa.2022.118516_b41","doi-asserted-by":"crossref","unstructured":"Yang, Z. C., Yang, D. Y., Dyer, C., He, X. D., Smola, A., & Hovy, E. H. (2016). Hierarchical attention networks for document classification. In Proceedings of the 2016 conference of the north American chapter of the association for computational linguistics: human language technologiesl (pp. 1480\u20131489).","DOI":"10.18653\/v1\/N16-1174"},{"key":"10.1016\/j.eswa.2022.118516_b42","doi-asserted-by":"crossref","unstructured":"Yi, X. W., Zhang, J. B., Wang, Z. Y., Li, T. R., & Zheng, Y. (2018). Deep distributed fusion network for air quality prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery& data mining (pp. 965\u2013973).","DOI":"10.1145\/3219819.3219822"},{"key":"10.1016\/j.eswa.2022.118516_b43","first-page":"1","article-title":"A deep multivariate time series multistep forecasting network","author":"Yin","year":"2021","journal-title":"Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies"},{"key":"10.1016\/j.eswa.2022.118516_b44","doi-asserted-by":"crossref","unstructured":"Yin, J. M., Rao, W. X., Yuan, M. X., Zeng, J., Zhao, K., Zhang, C. X., Li, J. F., & Zhao, Q. P. (2019). Experimental study of multivariate time series forecasting models. In Proceedings of the 28th ACM international conference on information and knowledge management (pp. 2833\u20132839).","DOI":"10.1145\/3357384.3357826"},{"key":"10.1016\/j.eswa.2022.118516_b45","doi-asserted-by":"crossref","unstructured":"Yuan, Z. N., Zhou, X., & Yang, T. B. (2018). Hetero-ConvLSTM: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery& data mining (pp. 984\u2013992).","DOI":"10.1145\/3219819.3219922"},{"key":"10.1016\/j.eswa.2022.118516_b46","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1016\/j.knosys.2018.10.025","article-title":"Parallel computing method of deep belief networks and its application to traffic flow prediction","volume":"163","author":"Zhao","year":"2019","journal-title":"Knowledge-Based Systems"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417422015950?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417422015950?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T15:48:04Z","timestamp":1701791284000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417422015950"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":46,"alternative-id":["S0957417422015950"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2022.118516","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-step forecasting of multivariate time series using multi-attention collaborative network","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2022.118516","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"118516"}}