{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:08:20Z","timestamp":1767139700664,"version":"build-2238731810"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819665907","type":"print"},{"value":"9789819665914","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\/978-981-96-6591-4_10","type":"book-chapter","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T08:38:57Z","timestamp":1750667937000},"page":"136-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["STEncoder: Robust Decomposition for\u00a0Time Series Forecasting"],"prefix":"10.1007","author":[{"given":"Junfeng","family":"Liao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riquan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,24]]},"reference":[{"key":"10_CR1","unstructured":"Krake, T., Kl\u00f6tzl, D., H\u00e4gele, D., Weiskopf, D.: Uncertainty-aware seasonal-trend decomposition based on loess. IEEE Trans. Visualizat. Comput. Graph., 1\u201316 (2024)"},{"issue":"2","key":"10_CR2","first-page":"719","volume":"16","author":"Y Zhang","year":"2015","unstructured":"Zhang, Y., Haghani, A., Zeng, X.: Component garch models to account for seasonal patterns and uncertainties in travel-time prediction. IEEE Trans. Intell. Transp. Syst. 16(2), 719\u2013729 (2015)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10_CR3","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1016\/j.scitotenv.2014.10.052","volume":"505","author":"E Ascari","year":"2015","unstructured":"Ascari, E., Licitra, G., Teti, L., Cerchiai, M.: Low frequency noise impact from road traffic according to different noise prediction methods. Sci. Total Environ. 505, 658\u2013669 (2015)","journal-title":"Sci. Total Environ."},{"issue":"1","key":"10_CR4","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s10515-024-00435-y","volume":"31","author":"J Shen","year":"2024","unstructured":"Shen, J., Li, Z., Lu, Y., Pan, M., Li, X.: Mitigating the impact of mislabeled data on deep predictive models: an empirical study of learning with noise approaches in software engineering tasks. Autom. Softw. Eng. 31(1), 33 (2024)","journal-title":"Autom. Softw. Eng."},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Chen, F., Chen, Z., Biswas, S., Lei, S., Ramakrishnan, N., Lu, C.T.: Graph convolutional networks with kalman filtering for traffic prediction. In: Proceedings of the 28th International Conference on Advances in Geographic Information Systems, pp. 135\u2013138 (2020)","DOI":"10.1145\/3397536.3422257"},{"key":"10_CR6","first-page":"22419","volume":"34","author":"H Wu","year":"2021","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Adv. Neural. Inf. Process. Syst. 34, 22419\u201322430 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 11121\u201311128 (2023)","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"10_CR8","unstructured":"Park, J., Gwak, D., Choo, J., Choi, E.: Self-supervised contrastive forecasting. arXiv preprint arXiv:2402.02023 (2024)"},{"key":"10_CR9","unstructured":"Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., Xiao, Y.: Micn: Multi-scale local and global context modeling for long-term series forecasting (2023)"},{"key":"10_CR10","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In: International Conference on Machine Learning, pp. 27268\u201327286. PMLR (2022)"},{"key":"10_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2023.109005","volume":"209","author":"S Xiang","year":"2023","unstructured":"Xiang, S., Liang, Q.: Remote sensing image compression with long-range convolution and improved non-local attention model. Signal Process. 209, 109005 (2023)","journal-title":"Signal Process."},{"key":"10_CR12","unstructured":"Liu, Y., et al.: itransformer: inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625 (2023)"},{"key":"10_CR13","unstructured":"Li, K., et al.: Uniformer: unified transformer for efficient spatiotemporal representation learning. arXiv preprint arXiv:2201.04676 (2022)"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Li, S., Chowdhury, R.R., Shang, J., Gupta, R.K., Hong, D.: Units: short-time fourier inspired neural networks for sensory time series classification. In: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, pp. 234\u2013247 (2021)","DOI":"10.1145\/3485730.3485942"},{"key":"10_CR15","unstructured":"Rasul, K., Seward, C., Schuster, I., Vollgraf, R.: Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In: International Conference on Machine Learning, pp. 8857\u20138868. PMLR (2021)"},{"key":"10_CR16","doi-asserted-by":"publisher","first-page":"7244","DOI":"10.1109\/ACCESS.2020.2963953","volume":"8","author":"J Chen","year":"2020","unstructured":"Chen, J., Li, X., Mohamed, M.A., Jin, T.: An adaptive matrix pencil algorithm based-wavelet soft-threshold denoising for analysis of low frequency oscillation in power systems. IEEE Access 8, 7244\u20137255 (2020)","journal-title":"IEEE Access"},{"key":"10_CR17","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s11760-020-01722-3","volume":"15","author":"S Lei","year":"2021","unstructured":"Lei, S., Lu, M., Lin, J., Zhou, X., Yang, X.: Remote sensing image denoising based on improved semi-soft threshold. Signal, Image Video Process. 15, 73\u201381 (2021)","journal-title":"Signal, Image Video Process."},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Li, D., Shen, W., Wu, J.: Optimization of threshold selection for wavelet-based denoising in conference scenarios. In: 2023 IEEE 23rd International Conference on Communication Technology (ICCT), pp. 247\u2013253. IEEE (2023)","DOI":"10.1109\/ICCT59356.2023.10419732"},{"key":"10_CR19","first-page":"12677","volume":"35","author":"T Zhou","year":"2022","unstructured":"Zhou, T., et al.: Film: frequency improved legendre memory model for long-term time series forecasting. Adv. Neural. Inf. Process. Syst. 35, 12677\u201312690 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR20","first-page":"28","volume":"1991","author":"T Edwards","year":"1991","unstructured":"Edwards, T.: Discrete wavelet transforms: Theory and implementation. Universidad de 1991, 28\u201335 (1991)","journal-title":"Universidad de"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Cai, C., Harrington, P.d.B.: Different discrete wavelet transforms applied to denoising analytical data. J. Chem. Inform. Comput. Sci. 38(6), 1161\u20131170 (1998)","DOI":"10.1021\/ci980210j"},{"issue":"6","key":"10_CR22","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1109\/78.923297","volume":"49","author":"S Sardy","year":"2001","unstructured":"Sardy, S., Tseng, P., Bruce, A.: Robust wavelet denoising. IEEE Trans. Signal Process. 49(6), 1146\u20131152 (2001)","journal-title":"IEEE Trans. Signal Process."},{"key":"10_CR23","doi-asserted-by":"publisher","first-page":"3862","DOI":"10.1109\/ACCESS.2016.2587581","volume":"4","author":"M Srivastava","year":"2016","unstructured":"Srivastava, M., Anderson, C.L., Freed, J.H.: A new wavelet denoising method for selecting decomposition levels and noise thresholds. IEEE Access 4, 3862\u20133877 (2016)","journal-title":"IEEE Access"},{"key":"10_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109050","volume":"134","author":"C Tian","year":"2023","unstructured":"Tian, C., Zheng, M., Zuo, W., Zhang, B., Zhang, Y., Zhang, D.: Multi-stage image denoising with the wavelet transform. Pattern Recogn. 134, 109050 (2023)","journal-title":"Pattern Recogn."},{"key":"10_CR25","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inform. Process. Syst. 30 (2017)"},{"key":"10_CR26","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: Timesnet: temporal 2d-variation modeling for general time series analysis. In: International Conference on Learning Representations (2023)"},{"key":"10_CR27","unstructured":"Nie, Y., Nguyen, N.H, Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: Long-term forecasting with transformers. In: International Conference on Learning Representations (2023)"},{"key":"10_CR28","first-page":"9881","volume":"35","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Wu, H., Wang, J., Long, M.: Non-stationary transformers: exploring the stationarity in time series forecasting. Adv. Neural. Inf. Process. Syst. 35, 9881\u20139893 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR29","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)"},{"key":"10_CR30","unstructured":"Zhang, Y., Yan, J.: Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: International Conference on Learning Representations (2023)"},{"key":"10_CR31","unstructured":"Woo, G., Liu, C., Sahoo, D., Kumar, A., Hoi, S.: Etsformer: Exponential smoothing transformers for time-series forecasting. arXiv preprint arXiv:2202.01381 (2022)"},{"issue":"10","key":"10_CR32","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.1109\/TIP.2010.2050107","volume":"19","author":"MD Robinson","year":"2010","unstructured":"Robinson, M.D., Toth, C.A., Lo, J.Y., Farsiu, S.: Efficient fourier-wavelet super-resolution. IEEE Trans. Image Process. 19(10), 2669\u20132681 (2010)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.130225","volume":"290","author":"G Ban","year":"2024","unstructured":"Ban, G., Chen, Y., Xiong, Z., Zhuo, Y., Huang, K.: The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved autoformer. Energy 290, 130225 (2024)","journal-title":"Energy"},{"key":"10_CR34","unstructured":"Toner, W., Darlow, L.: An analysis of linear time series forecasting models. arXiv preprint arXiv:2403.14587 (2024)"}],"updated-by":[{"DOI":"10.1007\/978-981-96-6591-4_27","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T00:00:00Z","timestamp":1766448000000}}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6591-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T04:43:54Z","timestamp":1766378634000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6591-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819665907","9789819665914"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6591-4_10","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":"24 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"23 December 2025","order":2,"name":"change_date","label":"Change Date","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"A correction has been published.","order":4,"name":"change_details","label":"Change Details","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"}}]}}