{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T13:19:40Z","timestamp":1775135980974,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819584161","type":"print"},{"value":"9789819584178","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-8417-8_33","type":"book-chapter","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T12:31:14Z","timestamp":1775133074000},"page":"455-467","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multivariate Time Series Forecasting Framework Based on Multi-scale Convolution and\u00a0an\u00a0Inverted Transformer with\u00a0Differencing Mechanism"],"prefix":"10.1007","author":[{"given":"Mengbo","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyan","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,3]]},"reference":[{"issue":"1","key":"33_CR1","doi-asserted-by":"publisher","first-page":"213","DOI":"10.24191\/myse.v9i1.17301","volume":"9","author":"II Iliyasu","year":"2022","unstructured":"Iliyasu, I.I., Abdullah, A., Marzbali, M.H.: Urban morphology and crime patterns in urban areas: a review of the literature. Malays. J. Sustain. Environ. (MySE) 9(1), 213\u2013242 (2022)","journal-title":"Malays. J. Sustain. Environ. (MySE)"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Degadwala, S., Vyas, D., Raval, A.R., et al.: Crime pattern analysis and prediction using regression models. In: 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), pp. 771\u2013776. IEEE (2023)","DOI":"10.1109\/ICSSAS57918.2023.10331747"},{"issue":"2","key":"33_CR3","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1007\/s10708-022-10684-7","volume":"88","author":"P Kabiraj","year":"2023","unstructured":"Kabiraj, P.: Crime in India: a spatio-temporal analysis. GeoJournal 88(2), 1283\u20131304 (2023)","journal-title":"GeoJournal"},{"issue":"6","key":"33_CR4","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3390\/ijgi12060209","volume":"12","author":"Y Du","year":"2023","unstructured":"Du, Y., Ding, N.: A systematic review of multi-scale spatio-temporal crime prediction methods. ISPRS Int. J. Geo Inf. 12(6), 209 (2023)","journal-title":"ISPRS Int. J. Geo Inf."},{"key":"33_CR5","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.neucom.2021.05.008","volume":"455","author":"G Jin","year":"2021","unstructured":"Jin, G., Sha, H., Feng, Y., et al.: GSEN: an ensemble deep learning benchmark model for urban hotspots spatiotemporal prediction. Neurocomputing 455, 353\u2013367 (2021)","journal-title":"Neurocomputing"},{"key":"33_CR6","first-page":"1","volume":"10","author":"SEM van Sleeuwen","year":"2021","unstructured":"van Sleeuwen, S.E.M., Ruiter, S., Steenbeek, W.: Right place, right time? Making crime pattern theory time-specific. Crime Sci. 10, 1\u201310 (2021)","journal-title":"Crime Sci."},{"issue":"1","key":"33_CR7","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1177\/2399808319846904","volume":"48","author":"J Corcoran","year":"2021","unstructured":"Corcoran, J., Zahnow, R., Kimpton, A., et al.: The temporality of place: constructing a temporal typology of crime in commercial precincts. Environ. Plan. B Urban Anal. City Sci. 48(1), 9\u201324 (2021)","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"issue":"3","key":"33_CR8","doi-asserted-by":"publisher","first-page":"152","DOI":"10.3390\/ijgi10030152","volume":"10","author":"M Yang","year":"2021","unstructured":"Yang, M., Chen, Z., Zhou, M., et al.: The impact of COVID-19 on crime: a spatial temporal analysis in Chicago. ISPRS Int. J. Geo-Inf. 10(3), 152 (2021)","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Zeng, A., Chen, M., Zhang, L., et al.: Are transformers effective for time series forecasting? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 9, pp. 11121\u201311128 (2023)","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"33_CR10","unstructured":"Liu, Y., Hu, T., Zhang, H., et al.: iTransformer: Inverted Transformers are Effective for Time Series Forecasting. arXiv Preprint arXiv:2310.06625 (2023)"},{"key":"33_CR11","unstructured":"Nie, Y., Nguyen, N.H., Sinthong, P., et al.: A Time Series Is Worth 64 Words: Long-Term Forecasting with Transformers. arXiv Preprint arXiv:2211.14730 (2022)"},{"key":"33_CR12","unstructured":"Chen, P., Zhang, Y., Cheng, Y., et al.: Pathformer: Multi-Scale Transformers with Adaptive Pathways for Time Series Forecasting. arXiv Preprint arXiv:2402.05956 (2024)"},{"key":"33_CR13","unstructured":"Zhang, Y., Yan, J.: Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"33_CR14","unstructured":"Wang, S., Wu, H., Shi, X., et al.: Timemixer: decomposable multiscale mixing for time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 2405.14616 (2024)"},{"issue":"18","key":"33_CR15","doi-asserted-by":"publisher","first-page":"13109","DOI":"10.1007\/s00521-021-05958-z","volume":"35","author":"J Fan","year":"2023","unstructured":"Fan, J., Zhang, K., Huang, Y., et al.: Parallel spatio-temporal attention-based TCN for multivariate time series prediction. Neural Comput. Appl. 35(18), 13109\u201313118 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"9","key":"33_CR16","first-page":"4659","volume":"44","author":"X Chen","year":"2021","unstructured":"Chen, X., Sun, L.: Bayesian temporal factorization for multidimensional time series prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 4659\u20134673 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Challu, C., Olivares, K.G., Oreshkin, B.N., et al.: NHITS: neural hierarchical interpolation for time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 6, pp. 6989\u20136997 (2023)","DOI":"10.1609\/aaai.v37i6.25854"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 12, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Qin, G., Huang, X., et al.: Autotimes: autoregressive time series forecasters via large language models. In: Advances in Neural Information Processing Systems, vol. 37, pp. 122154\u2013122184 (2024)","DOI":"10.52202\/079017-3882"},{"key":"33_CR20","doi-asserted-by":"crossref","unstructured":"Tan, M., Merrill, M., Gupta, V., et al.: Are language models actually useful for time series forecasting? In: Advances in Neural Information Processing Systems, vol. 37, pp. 60162\u201360191 (2024)","DOI":"10.52202\/079017-1922"},{"key":"33_CR21","unstructured":"Rasul, K., Seward, C., Schuster, I., et al.: Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In: International Conference on Machine Learning, pp. 8857\u20138868. PMLR (2021)"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Cheng, M., Yang, J., Pan, T., et al.: Convtimenet: a deep hierarchical fully convolutional model for multivariate time series analysis. In: Companion Proceedings of the ACM on Web Conference, pp. 171\u2013180 (2025)","DOI":"10.1145\/3701716.3715214"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Lin, S., Lin, W., Hu, X., et al.: Cyclenet: enhancing time series forecasting through modeling periodic patterns. In: Advances in Neural Information Processing Systems, vol. 37, pp. 106315\u2013106345 (2024)","DOI":"10.52202\/079017-3373"},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Feng, S., Miao, C., Zhang, Z., et al.: Latent diffusion transformer for probabilistic time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 11, pp. 11979\u201311987 (2024)","DOI":"10.1609\/aaai.v38i11.29085"}],"container-title":["Lecture Notes in Computer Science","Algorithms and Architectures for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-8417-8_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T12:31:18Z","timestamp":1775133078000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-8417-8_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819584161","9789819584178"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-8417-8_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"3 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICA3PP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Algorithms and Architectures for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ica3pp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ieee-cybermatics.org\/2025\/ica3pp\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}