{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T04:01:18Z","timestamp":1749355278879,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":41,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819665754","type":"print"},{"value":"9789819665761","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-6576-1_17","type":"book-chapter","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T05:39:05Z","timestamp":1749274745000},"page":"241-255","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EL-LSTM: A Multivariate Time Series Forecasting Model Combining Spiking Neurons and\u00a0Long Short-Term Memory Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3143-6563","authenticated-orcid":false,"given":"Lei","family":"Yang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8344-7928","authenticated-orcid":false,"given":"Yuhan","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8066-3261","authenticated-orcid":false,"given":"Kaixin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Pinjie","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Kangshun","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,8]]},"reference":[{"issue":"8","key":"17_CR1","doi-asserted-by":"publisher","first-page":"7665","DOI":"10.1109\/TKDE.2022.3218803","volume":"35","author":"J Deng","year":"2023","unstructured":"Deng, J., Chen, X., Jiang, R., Song, X., Tsang, I.W.: A multi-view multi-task learning framework for multi-variate time series forecasting. IEEE Trans. Knowl. Data Eng. 35(8), 7665\u20137680 (2023). https:\/\/doi.org\/10.1109\/TKDE.2022.3218803","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"10","key":"17_CR2","doi-asserted-by":"publisher","first-page":"7034","DOI":"10.1109\/TNNLS.2021.3137178","volume":"34","author":"W Zheng","year":"2023","unstructured":"Zheng, W., Hu, J.: Multivariate time series prediction based on temporal change information learning method. IEEE Trans. Neural Netw. Learn. Syst. 34(10), 7034\u20137048 (2023). https:\/\/doi.org\/10.1109\/TNNLS.2021.3137178","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"17_CR3","doi-asserted-by":"publisher","unstructured":"Wu, Z., et al.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD Conference Knowledge Discovery Data Mining, pp. 753\u2013763 (2020). https:\/\/doi.org\/10.1145\/3394486","DOI":"10.1145\/3394486"},{"key":"17_CR4","doi-asserted-by":"publisher","unstructured":"Trinh, N.P.A., Tran, K.N., Do, T.H.: Traffic flow forecasting using multivariate time-series deep learning and distributed computing. In: 2022 RIVF International Conference on Computing and Communication Technologies (RIVF), pp. 665\u2013670. Ho Chi Minh City (2022). https:\/\/doi.org\/10.1109\/RIVF55975.2022.10013796","DOI":"10.1109\/RIVF55975.2022.10013796"},{"key":"17_CR5","doi-asserted-by":"publisher","unstructured":"Verma, S.K., Gupta, A., Jyoti, A.: Stack layer and bidirectional layer long short - term memory (LSTM) time series model with intermediate variable for weather prediction. In: International Conference on Computational Performance Evaluation (ComPE), pp. 065\u2013070. Shillong (2021). https:\/\/doi.org\/10.1109\/ComPE53109.2021.9752357","DOI":"10.1109\/ComPE53109.2021.9752357"},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Ahmed, M., Abdelrazek, S., Kamalasadan, S., Enslin, J., Fenimore, T.: Weather forecasting based intelligent distribution feeder load prediction. In: IEEE Power and Energy Society General Meeting (PESGM), pp. 1\u20135. Boston (2016). https:\/\/doi.org\/10.1109\/PESGM.2016.7741878","DOI":"10.1109\/PESGM.2016.7741878"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Zlatkova, A., Velkovski, B., Kokolanski, Z., Taskovski, D.: Short-term energy forecasting for public educational institution. In: IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC \/ I and CPS Europe), pp. 1\u20136. Madrid (2023)","DOI":"10.1109\/EEEIC\/ICPSEurope57605.2023.10194706"},{"issue":"1","key":"17_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-020-09773-x","volume":"13","author":"M Mahmud","year":"2021","unstructured":"Mahmud, M., et al.: Deep learning in mining biological data. Cogn. Comput. 13(1), 1\u201333 (2021)","journal-title":"Cogn. Comput."},{"key":"17_CR9","doi-asserted-by":"publisher","unstructured":"Qin, Y., et al.: A dual-stage attention-based recurrent neural network for time series prediction. In: Proceedings 26th International Joint Conference Artificial Intelligence, pp. 2627\u20132633 (2017). https:\/\/doi.org\/10.48550\/arXiv.1704.02971","DOI":"10.48550\/arXiv.1704.02971"},{"key":"17_CR10","doi-asserted-by":"publisher","unstructured":"Widiasari, I.R., Nugroho, L.E., Widyawan: deep learning multilayer perceptron (MLP) for flood prediction model using wireless sensor network based hydrology time series data mining. In: International Conference on Innovative and Creative Information Technology (ICITech), pp. 1\u20135. Salatiga (2017). https:\/\/doi.org\/10.1109\/INNOCIT.2017.8319150","DOI":"10.1109\/INNOCIT.2017.8319150"},{"key":"17_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-030-59277-6_27","volume-title":"Brain Informatics","author":"MS Satu","year":"2020","unstructured":"Satu, M.S., Rahman, S., Khan, M.I., Abedin, M.Z., Kaiser, M.S., Mahmud, M.: Towards improved detection of cognitive performance using bidirectional multilayer long-short term memory neural network. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds.) BI 2020. LNCS (LNAI), vol. 12241, pp. 297\u2013306. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59277-6_27"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Madan, R., Mangipudi, P.S.: Predicting computer network traffic: a time series forecasting approach using DWT, ARIMA and RNN. In: Eleventh International Conference on Contemporary Computing (IC3), pp. 1\u20135. Noida (2018)","DOI":"10.1109\/IC3.2018.8530608"},{"key":"17_CR13","doi-asserted-by":"publisher","unstructured":"Sbrana, A., Debiaso Rossi, A.L., Coelho Naldi, M.: N-BEATS-RNN: deep learning for time series forecasting. In: IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 765\u2013768. Miami(2020). https:\/\/doi.org\/10.1109\/ICMLA51294.2020.00125","DOI":"10.1109\/ICMLA51294.2020.00125"},{"key":"17_CR14","doi-asserted-by":"publisher","unstructured":"Rokui, J.: Historical time series prediction framework based on recurrent neural network using multivariate time series. In: International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 486\u2013489. Niigata (2021). https:\/\/doi.org\/10.1109\/IIAI-AAI53430.2021.00084","DOI":"10.1109\/IIAI-AAI53430.2021.00084"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Ludwig, S.A.: Comparison of time series approaches applied to greenhouse gas analysis: ANFIS, RNN, and LSTM. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1\u20136. New Orleans (2019)","DOI":"10.1109\/FUZZ-IEEE.2019.8859013"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Saini, K., Sharma, S.: Gated recurrent unit (GRU) in RNN for traffic forecasting based on time-series data. In: International Conference on Innovative Sustainable Computational Technologies (CISCT), pp. 1\u20134. Dehradun(2022)","DOI":"10.1109\/CISCT55310.2022.10046484"},{"key":"17_CR17","doi-asserted-by":"publisher","unstructured":"Hu, Y., Wang, N., Lyu, L., Zhou, X., Fang, M.: Application of Seq2Seq model based on TCN-GRU to multivariate water quality time series prediction. In: International Conference of Information and Communication Technology (ICTech), pp. 201\u2013205. China (2023). https:\/\/doi.org\/10.1109\/ICTech58362.2023.00058","DOI":"10.1109\/ICTech58362.2023.00058"},{"key":"17_CR18","doi-asserted-by":"publisher","unstructured":"Mohapatra, S.K., Mishra, S., Tripathy, H.K.: Energy consumption prediction in electrical appliances of commercial buildings using LSTM-GRU Model. In: International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), pp. 1\u20135. Bhubaneswar (2022). https:\/\/doi.org\/10.1109\/ASSIC55218.2022.10088334","DOI":"10.1109\/ASSIC55218.2022.10088334"},{"key":"17_CR19","unstructured":"Riemer, M., et al.: Correcting forecasts with multifactor neural attention. In: International Conference on Machine Learning. PMLR, pp. 3010\u20133019 (2016)"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Hu, J., Zheng, W.: Multistage attention network for multivariate time series prediction. Neurocomputing, 122\u2013137 (2020)","DOI":"10.1016\/j.neucom.2019.11.060"},{"key":"17_CR21","doi-asserted-by":"publisher","unstructured":"Zheng, W., Zhao, P., Chen, G., Zhou, H., Tian, Y.: A hybrid spiking neurons embedded LSTM network for multivariate time series learning under concept-drift environment. IEEE Trans. Knowl. Data Eng., 6561\u20136574 (2023). https:\/\/doi.org\/10.1109\/TKDE.2022.3178176","DOI":"10.1109\/TKDE.2022.3178176"},{"key":"17_CR22","unstructured":"Bellec, G., et al.: Long short-term memory and learning-to-learn in networks of spiking neurons. In: Proceedings Advance Neural Information Processing Systems, pp. 795\u2013805 (2018)"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Yin, B., et al.: Effective and efficient computation with multiple timescale spiking recurrent neural networks. In: Proceedings International Conference Neuromorphic Systems, pp. 1:1\u20131:8 (2020)","DOI":"10.1145\/3407197.3407225"},{"key":"17_CR24","doi-asserted-by":"publisher","unstructured":"Fang, H., et al.: Exploiting neuron and synapse filter dynamics in spatial temporal learning of deep spiking neural network. In: Proceedings of the 29th International Joint Conference Artificial Intelligence, pp. 2799\u20132806 (2020). https:\/\/doi.org\/10.48550\/arXiv.2003.02944","DOI":"10.48550\/arXiv.2003.02944"},{"key":"17_CR25","unstructured":"Zhang, W., Li, P.: Temporal spike sequence learning via backpropagation for deep spiking neural networks. In: Advance Neural Information Processing Systems, Art (2020)"},{"key":"17_CR26","doi-asserted-by":"publisher","unstructured":"Wu, Y., et al.: Direct training for spiking neural networks: faster, larger, better. In: Proceedings of the 33rd AAAI Conference Artificial Intelligence, pp. 1311\u20131318 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33011311","DOI":"10.1609\/aaai.v33i01.33011311"},{"key":"17_CR27","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.neunet.2020.08.001","volume":"132","author":"W He","year":"2020","unstructured":"He, W., et al.: Comparing SNNs and RNNs on neuromorphic vision datasets: similarities and differences. Neural Netw. 132, 108\u2013120 (2020)","journal-title":"Neural Netw."},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Zheng, H., et al.: Going deeper with directly-trained larger spiking neural networks. In: Proceedings of the 35th AAAI Conference Artificial Intelligence, pp. 11062\u201311070 (2021)","DOI":"10.1609\/aaai.v35i12.17320"},{"issue":"1","key":"17_CR29","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1109\/TNNLS.2018.2833077","volume":"30","author":"M Zhang","year":"2019","unstructured":"Zhang, M., Qu, H., Belatreche, A., Chen, Y., Yi, Z.: A highly effective and robust membrane potential-driven supervised learning method for spiking neurons. IEEE Trans. Neural Netw. Learn. Syst. 30(1), 123\u2013137 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"17_CR30","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1109\/TNNLS.2018.2868874","volume":"30","author":"A Jeyasothy","year":"2019","unstructured":"Jeyasothy, A., Sundaram, S., Sundararajan, N.: SEFRON: a new spiking neuron model with time-varying synaptic efficacy function for pattern classification. IEEE Trans. Neural Netw. Learn. Syst. 30(4), 1231\u20131240 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"17_CR31","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511815706","volume-title":"Spiking Neuron Models: Single Neurons, Populations, Plasticity","author":"W Gerstner","year":"2002","unstructured":"Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge, U.K. (2002)"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Gerum, R., et al.: Leaky-integrate-and-fire neuron-like long-short-term-memory units as model system in computational biology. In: 2023 International Joint Conference on Neural Networks (IJCNN). IEEE (2023)","DOI":"10.1109\/IJCNN54540.2023.10191268"},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Wu, Z., et al.: LIAF-Net: leaky integrate and analog fire network for lightweight and efficient spatiotemporal information processing. IEEE Trans. Neural Netw. Learn. Syst. (2021)","DOI":"10.1109\/TNNLS.2021.3073016"},{"key":"17_CR34","doi-asserted-by":"crossref","unstructured":"Lu, J., et al.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng., 2346\u20132363(2018)","DOI":"10.1109\/TKDE.2018.2876857"},{"key":"17_CR35","doi-asserted-by":"publisher","unstructured":"Ponghiran, W., Roy, K.: Hybrid analog-spiking long short-term memory for energy efficient computing on edge devices. In: Proceedings Design, Automation and Test in Europe Conference and Exhibition (DATE), pp. 581\u2013586 (2021). https:\/\/doi.org\/10.23919\/DATE51398.2021.9473953","DOI":"10.23919\/DATE51398.2021.9473953"},{"key":"17_CR36","unstructured":"John, H.: Metro interstate traffic volume data set. UCI (2019). https:\/\/archive-beta.ics.uci.edu\/ml\/datasets\/metrointerstatetraffic_volume"},{"key":"17_CR37","doi-asserted-by":"publisher","unstructured":"Liang, X. et al.: Assessing Beijing\u2019s PM2.5 pollution: severity, weather impact, APEC and winter heating. In: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 471, no. 20150257 (2015). https:\/\/doi.org\/10.1098\/rspa.2015.0257","DOI":"10.1098\/rspa.2015.0257"},{"key":"17_CR38","doi-asserted-by":"publisher","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput., 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"17_CR39","doi-asserted-by":"publisher","unstructured":"Chung, J., et al.: Empirical evaluation of gated recurrent neural networks on sequence modeling (2014). https:\/\/doi.org\/10.48550\/arXiv.1412.3555","DOI":"10.48550\/arXiv.1412.3555"},{"key":"17_CR40","doi-asserted-by":"crossref","unstructured":"Salinas, D., et al.: DeepAR: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast., 1181\u20131191 (2020)","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"17_CR41","unstructured":"Zhao, J., et al.: Do RNN and LSTM have long memory. In: Proceedings of the 36th International Conference Machine Learning, pp. 11365\u201311375 (2020)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6576-1_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T05:39:17Z","timestamp":1749274757000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6576-1_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819665754","9789819665761"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6576-1_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"8 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}