{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T12:58:47Z","timestamp":1764075527443,"version":"3.45.0"},"publisher-location":"New York, NY, USA","reference-count":14,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,26]]},"DOI":"10.1145\/3760622.3760649","type":"proceedings-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T12:55:52Z","timestamp":1764075352000},"page":"86-90","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["STL-Transformer: A Hybrid Model for Financial Time Series Forecasting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1618-6339","authenticated-orcid":false,"given":"Kexin","family":"Peng","sequence":"first","affiliation":[{"name":"Kyoto Institute of Technology, Kyoto, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9596-8679","authenticated-orcid":false,"given":"Hitoshi","family":"Iima","sequence":"additional","affiliation":[{"name":"Kyoto Institute of Technology, Kyoto, Japan"}]}],"member":"320","published-online":{"date-parts":[[2025,11,25]]},"reference":[{"key":"e_1_3_3_2_2_2","first-page":"2672","volume-title":"Advances in Neural Information Processing Systems","author":"Goodfellow I.\u00a0J.","year":"2014","unstructured":"I.\u00a0J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. 2672\u20132680."},{"key":"e_1_3_3_2_3_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Kingma D.\u00a0P.","year":"2014","unstructured":"D.\u00a0P. Kingma and M. Welling. 2014. Auto-encoding variational Bayes. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_2_4_2","first-page":"5998","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani A.","year":"2017","unstructured":"A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.\u00a0N. Gomez, \u0141. Kaiser, and I. Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998\u20136008."},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"D.\u00a0E. Rumelhart G.\u00a0E. Hinton and R.\u00a0J. Williams. 1986. Learning representations by back-propagating errors. Nature 323 6088 (Oct 1986) 533\u2013536.","DOI":"10.1038\/323533a0"},{"key":"e_1_3_3_2_6_2","unstructured":"R.\u00a0B. Cleveland W.\u00a0S. Cleveland J.\u00a0E. McRae and I. Terpenning. 1990. STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics 6 1 (1990) 3\u201373."},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"B.\u00a0S. Bernanke and K.\u00a0N. Kuttner. 2005. What explains the stock market\u2019s reaction to Federal Reserve policy? The Journal of Finance LX 3 (Jun 2005) 1221\u20131257.","DOI":"10.1111\/j.1540-6261.2005.00760.x"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural Computation 9 8 (Nov 1997) 1735\u20131780.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"crossref","unstructured":"C. Wang Y. Chen S. Zhang and Q. Zhang. 2022. Stock market index prediction using deep Transformer model. Expert Systems with Applications 208 (2022) 118128.","DOI":"10.1016\/j.eswa.2022.118128"},{"key":"e_1_3_3_2_10_2","first-page":"1062","volume-title":"2024 SICE Festival with Annual Conference (SICE FES)","author":"Peng K.","year":"2024","unstructured":"K. Peng, H. Iima, and Y. Kitamura. 2024. Prediction of foreign exchange rates by a large language model. In 2024 SICE Festival with Annual Conference (SICE FES). 1062\u20131066."},{"key":"e_1_3_3_2_11_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Cao D.","year":"2024","unstructured":"D. Cao, F. Jia, S.\u00a0\u00d6. Ar\u0131k, T. Pfister, Y. Zheng, W. Ye, and Y. Liu. 2024. TEMPO: Prompt-based generative pre-trained transformer for time series forecasting. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","unstructured":"S. Yang Z. Deng X. Li C. Zheng L. Xi J. Zhuang Z. Zhang and Z. Zhang. 2021. A novel hybrid model based on STL decomposition and one-dimensional convolutional neural networks with positional encoding for significant wave height forecast. Renewable Energy 173 (Aug 2021) 531\u2013543. 10.1016\/j.renene.2021.04.010","DOI":"10.1016\/j.renene.2021.04.010"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","unstructured":"H. He S. Gao T. Jin S. Sato and X. Zhang. 2021. A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction. Applied Soft Computing 108 (Sep 2021) 107488. 10.1016\/j.asoc.2021.107488","DOI":"10.1016\/j.asoc.2021.107488"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"N.\u00a0E. Huang Z. Shen S.\u00a0R. Long M.\u00a0C. Wu H.\u00a0H. Shih Q. Zheng N.-C. Yen C.\u00a0C. Tung and H.\u00a0H. Liu. 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical Physical and Engineering Sciences 454 1971 (Mar 1998) 903\u2013995.","DOI":"10.1098\/rspa.1998.0193"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"C. Spearman. 1904. The proof and measurement of association between two things. The American Journal of Psychology 15 1 (Jan 1904) 72\u2013101.","DOI":"10.2307\/1412159"}],"event":{"name":"ISMSI 2025: 2025 9th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","location":"Tokyo Japan","acronym":"ISMSI 2025"},"container-title":["Proceedings of the 2025 9th International Conference on Intelligent Systems, Metaheuristics &amp; Swarm Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3760622.3760649","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T12:56:43Z","timestamp":1764075403000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3760622.3760649"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,26]]},"references-count":14,"alternative-id":["10.1145\/3760622.3760649","10.1145\/3760622"],"URL":"https:\/\/doi.org\/10.1145\/3760622.3760649","relation":{},"subject":[],"published":{"date-parts":[[2025,4,26]]},"assertion":[{"value":"2025-11-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}