{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T20:08:08Z","timestamp":1778789288497,"version":"3.51.4"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031333828","type":"print"},{"value":"9783031333835","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-33383-5_25","type":"book-chapter","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T19:01:43Z","timestamp":1685386903000},"page":"314-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Targeted Attacks on\u00a0Time Series Forecasting"],"prefix":"10.1007","author":[{"given":"Zeyu","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katharina","family":"Dost","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinglong","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gillian","family":"Dobbie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00f6rg","family":"Wicker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s13042-010-0007-7","volume":"1","author":"B Biggio","year":"2010","unstructured":"Biggio, B., Fumera, G., Roli, F.: Multiple classifier systems for robust classifier design in adversarial environments. J. Mach. Learn. Cybern. 1, 27\u201341 (2010)","journal-title":"J. Mach. Learn. Cybern."},{"key":"25_CR2","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: ICML, pp. 1613\u20131622. PMLR (2015)"},{"key":"25_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"25_CR5","unstructured":"Cowtan, K.: The climate data guide: Global surface temperatures: berkeley earth surface temperatures (2019). https:\/\/bit.ly\/3fAqtVg Accessed 18 Feb 2022"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Dalvi, N., Domingos, P., Sanghai, S., Verma, D.: Adversarial classification. In: The tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 99\u2013108 (2004)","DOI":"10.1145\/1014052.1014066"},{"key":"25_CR7","unstructured":"Dang-Nhu, R., Singh, G., Bielik, P., Vechev, M.: Adversarial attacks on probabilistic autoregressive forecasting models. In: III, H.D., Singh, A. (eds.) The 37th ICML. vol. 119, pp. 2356\u20132365. PMLR, 13\u201318 Jul 2020"},{"key":"25_CR8","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1016\/j.rser.2017.02.085","volume":"74","author":"C Deb","year":"2017","unstructured":"Deb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W.: A review on time series forecasting techniques for building energy consumption. Renew. Sustain. Energy Rev. 74, 902\u2013924 (2017)","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"4","key":"25_CR9","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/TDSC.2017.2700270","volume":"16","author":"A Demontis","year":"2017","unstructured":"Demontis, A., et al.: Yes, machine learning can be more secure! A case study on android malware detection. IEEE Trans. Dependable Secure Comput. 16(4), 711\u2013724 (2017)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"issue":"8","key":"25_CR10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"8","key":"25_CR11","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","volume":"53","author":"G Van Houdt","year":"2020","unstructured":"Van Houdt, G., Mosquera, C., N\u00e1poles, G.: A review on the long short-term memory model. Artif. Intell. Rev. 53(8), 5929\u20135955 (2020). https:\/\/doi.org\/10.1007\/s10462-020-09838-1","journal-title":"Artif. Intell. Rev."},{"key":"25_CR12","unstructured":"Ko\u0142cz, A., Teo, C.H.: Feature weighting for improved classifier robustness. In: CEAS \u201909, Mountain View, CA, USA (2009)"},{"issue":"11","key":"25_CR13","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"IEEE"},{"key":"25_CR14","unstructured":"Liu, L., Park, Y., Hoang, T.N., Hasson, H., Huan, J.: Towards robust multivariate time-series forecasting: adversarial attacks and defense mechanisms. In: KDD 2022 Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications (2022)"},{"key":"25_CR15","unstructured":"Mathieu, E., et al.: Coronavirus pandemic (covid-19). Our World in Data (2020). https:\/\/ourworldindata.org\/coronavirus"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Mode, G.R., Hoque, K.A.: Adversarial examples in deep learning for multivariate time series regression. In: 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1\u201310. IEEE (2020)","DOI":"10.1109\/AIPR50011.2020.9425190"},{"issue":"2","key":"25_CR17","doi-asserted-by":"publisher","first-page":"13","DOI":"10.5121\/ijcsea.2014.4202","volume":"4","author":"P Mondal","year":"2014","unstructured":"Mondal, P., Shit, L., Goswami, S.: Study of effectiveness of time series modeling (arima) in forecasting stock prices. IJCSEA 4(2), 13 (2014)","journal-title":"IJCSEA"},{"key":"25_CR18","unstructured":"Razvan-Gabriel Cirstea, Chenjuan Guo, B.Y.: Graph attention recurrent neural networks for correlated time series forecasting. In: KDD MiLeTS19 (2019)"},{"key":"25_CR19","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533\u2013536 (1986)","journal-title":"Nature"},{"key":"25_CR20","unstructured":"Storn, R., Price, K.: Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces. J. Global Optim. 23 (1995)"},{"issue":"5","key":"25_CR21","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","volume":"23","author":"J Su","year":"2019","unstructured":"Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput. 23(5), 828\u2013841 (2019)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"25_CR22","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1016\/j.ins.2021.11.007","volume":"587","author":"T Wu","year":"2022","unstructured":"Wu, T., Wang, X., Qiao, S., Xian, X., Liu, Y., Zhang, L.: Small perturbations are enough: Adversarial attacks on time series prediction. Inf. Sci. 587, 794\u2013812 (2022)","journal-title":"Inf. Sci."},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Xu, A., Wang, X., Zhang, Y., Wu, T., Xian, X.: Adversarial attacks on deep neural networks for time series prediction. In: 2021 10th ICICSE, pp. 8\u201314 (2021)","DOI":"10.1145\/3485314.3485316"},{"key":"25_CR24","unstructured":"Yoon, Y., Swales, G.: Predicting stock price performance: a neural network approach. In: The Twenty-Fourth Annual Hawaii International Conference on System Sciences, vol. 4, pp. 156\u2013162 (1991)"},{"issue":"1","key":"25_CR25","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","volume":"14","author":"G Zhang","year":"1998","unstructured":"Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35\u201362 (1998)","journal-title":"Int. J. Forecast."},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: Traffic flow forecasting with spatial-temporal graph diffusion network. In: The AAAI Conference on Artificial Intelligence, vol. 35, pp. 15008\u201315015 (2021)","DOI":"10.1609\/aaai.v35i17.17761"},{"key":"25_CR27","doi-asserted-by":"publisher","unstructured":"Zhang, Z.: Multivariate Time Series Analysis in Climate and Environmental Research. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-67340-0","DOI":"10.1007\/978-3-319-67340-0"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-33383-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:08:58Z","timestamp":1710360538000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33383-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031333828","9783031333835"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33383-5_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Osaka","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"813","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"143","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}