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In order to optimize the accuracy of energy futures prices prediction, a new hybrid model is established in this paper which combines wavelet packet decomposition (WPD) based on long short\u2010term memory network (LSTM) with stochastic time effective weight (SW) function method (WPD\u2010SW\u2010LSTM). In the proposed framework, WPD is a signal processing method employed to decompose the original series into subseries with different frequencies and the SW\u2010LSTM model is constructed based on random theory and the principle of LSTM network. To investigate the prediction performance of the new forecasting approach, SVM, BPNN, LSTM, WPD\u2010BPNN, WPD\u2010LSTM, CEEMDAN\u2010LSTM, VMD\u2010LSTM, and ST\u2010GRU are considered as comparison models. Moreover, a new error measurement method (multiorder multiscale complexity invariant distance, MMCID) is improved to evaluate the forecasting results from different models, and the numerical results demonstrate that the high\u2010accuracy forecast of oil futures prices is realized.<\/jats:p>","DOI":"10.1155\/2021\/7653091","type":"journal-article","created":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T17:06:22Z","timestamp":1626195982000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A New Hybrid Forecasting Model Based on SW\u2010LSTM and Wavelet Packet Decomposition: A Case Study of Oil Futures Prices"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9689-0981","authenticated-orcid":false,"given":"Jie","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,7,13]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.najef.2019.01.011"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2009.09.015"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2012.04.001"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.117520"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2019.07.009"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.05.086"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2004.03.016"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.12.084"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2016.02.098"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113686"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.03.035"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.04.167"},{"key":"e_1_2_10_13_2","volume-title":"Theory, Design and Application of Artificial Neural Network","author":"Han L. 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