{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T23:16:19Z","timestamp":1777504579121,"version":"3.51.4"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030630065","type":"print"},{"value":"9783030630072","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-63007-2_20","type":"book-chapter","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:02:39Z","timestamp":1606089759000},"page":"255-266","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Analysing Effects of Customer Clustering for Customer\u2019s Account Balance Forecasting"],"prefix":"10.1007","author":[{"given":"Duy Hung","family":"Phan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quang Dat","family":"Do","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,23]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","unstructured":"Banerjee, D.: Forecasting of Indian stock market using time-series ARIMA model. In: Proceedings of the 2nd International Conference on Business and Information Management (ICBIM), Durgapur, pp. 131\u2013135 (2014). https:\/\/doi.org\/10.1109\/icbim.2014.6970973","DOI":"10.1109\/icbim.2014.6970973"},{"key":"20_CR2","doi-asserted-by":"publisher","unstructured":"Chen-Xu, N., Jie-Sheng, W.: Auto regressive moving average (ARMA) prediction method of bank cash flow time series. In: Proceedings of the 34th Chinese Control Conference (CCC), Hangzhou, pp. 4928\u20134933 (2015). https:\/\/doi.org\/10.1109\/chicc.2015.7260405","DOI":"10.1109\/chicc.2015.7260405"},{"key":"20_CR3","doi-asserted-by":"publisher","unstructured":"Wang, Z., Lou, Y.: Hydrological time series forecast model based on wavelet de-noising and ARIMA-LSTM. In: Proceedings of the IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China, pp. 1697\u20131701 (2019). https:\/\/doi.org\/10.1109\/itnec.2019.8729441","DOI":"10.1109\/itnec.2019.8729441"},{"key":"20_CR4","doi-asserted-by":"publisher","unstructured":"Agnieszka, D., Magdalena, L.: Detection of outliers in the financial time series using ARIMA models. In: Proceedings of the Applications of Electromagnetics in Modern Techniques and Medicine (PTZE), Rac\u0142awice, pp. 49\u201352 (2018). https:\/\/doi.org\/10.1109\/ptze.2018.8503260","DOI":"10.1109\/ptze.2018.8503260"},{"key":"20_CR5","doi-asserted-by":"publisher","unstructured":"Souhaib, B.T., Bonsoo, K.: Regularized regression for hierarchical forecasting without unbiasedness conditions. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019), pp. 1337\u20131347. ACM, New York (2019). https:\/\/doi.org\/10.1145\/3292500.3330976","DOI":"10.1145\/3292500.3330976"},{"key":"20_CR6","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.eswa.2019.06.060","volume":"137","author":"PK Juan","year":"2019","unstructured":"Juan, P.K., Sebasti\u00e1n, M.: Hierarchical time series forecasting via support vector regression in the European travel retail industry. Expert Syst. Appl. 137, 59\u201373 (2019). https:\/\/doi.org\/10.1016\/j.eswa.2019.06.060","journal-title":"Expert Syst. Appl."},{"key":"20_CR7","doi-asserted-by":"publisher","unstructured":"Liu, Z., Yan, Y., Yang, J., et al.: Missing value estimation for hierarchical time series: a study of hierarchical web traffic. In: Proceedings of the IEEE International Conference on Data Mining, Atlantic City, NJ, pp. 895\u2013900 (2015). https:\/\/doi.org\/10.1109\/icdm.2015.58","DOI":"10.1109\/icdm.2015.58"},{"key":"20_CR8","doi-asserted-by":"publisher","unstructured":"Wilms, H., Cupelli, M., Monti, A., et al.: Exploiting spatio-temporal dependencies for RNN-based wind power forecasts. In: Proceesings of the IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), Bangkok, Thailand, pp. 921\u2013926 (2019). https:\/\/doi.org\/10.1109\/gtdasia.2019.8715887","DOI":"10.1109\/gtdasia.2019.8715887"},{"key":"20_CR9","doi-asserted-by":"publisher","unstructured":"Koprinska, L., Wu, D., Wang, Z.: Convolutional neural networks for energy time series forecasting. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, pp. 1\u20138 (2018). https:\/\/doi.org\/10.1109\/ijcnn.2018.8489399","DOI":"10.1109\/ijcnn.2018.8489399"},{"key":"20_CR10","unstructured":"Data Set: (2019). https:\/\/github.com\/ziczacziczac\/customer-clustering\/tree\/master\/data"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"You, S.Y., Wang, Y.D., Luo, L.D., et al.: Finding the clusters with potential value in financial time series based on agglomerative hierarchical clustering. In: Proceedings of the 11th International Conference on Computer Science and Education, Nagoya, pp. 77\u201381 (2016)","DOI":"10.1109\/ICCSE.2016.7581558"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Talei, H., Essaaidi, M., Benhaddou, D.: An end to end real time architecture for analyzing and clustering time series data: case of an energy management system. In: Proceedings of the 6th International Renewable and Sustainable Energy Conference (IRSEC), Rabat, pp. 1\u20137 (2018)","DOI":"10.1109\/IRSEC.2018.8702839"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Wang, W., Lyu, G., Shi, Y., et al.: Time series clustering based on dynamic time warping. In: Proceedings of the IEEE 9th International Conference on Software Engineering and Service Science, Beijing, China, pp. 487\u2013490 (2018)","DOI":"10.1109\/ICSESS.2018.8663857"},{"key":"20_CR14","unstructured":"Cuturi, M., Blondel, M.: Soft-DTW: a differentiable loss function for time-series. arXiv preprint arXiv:1703.01541 (2017)"},{"key":"20_CR15","unstructured":"The R library for distance and density-based outlier detection (2019). https:\/\/cran.r-project.org\/web\/packages\/DDoutlier\/"},{"key":"20_CR16","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1016\/j.patcog.2010.09.013","volume":"44","author":"F Petitjean","year":"2011","unstructured":"Petitjean, F., Ketterlin, A., Gancarski, P.: A global averaging method for dynamic time warping, with applications to clustering. Pattern Recogn. 44, 678\u2013693 (2011)","journal-title":"Pattern Recogn."},{"key":"20_CR17","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987)","journal-title":"J. Comput. Appl. Math."},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Saitta, S., Raphael, B., Smith, I.F.: A bounded index for cluster validity. In: Proceedings of the International Workshop on Machine Learning and Data Mining in Pattern Recognition, pp. 174\u2013187 (2007)","DOI":"10.1007\/978-3-540-73499-4_14"},{"issue":"1","key":"20_CR19","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.patcog.2012.07.021","volume":"46","author":"O Arbelaitz","year":"2013","unstructured":"Arbelaitz, O., Gurrutxaga, I., Muguerza, J., et al.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243\u2013256 (2013)","journal-title":"Pattern Recogn."},{"issue":"6","key":"20_CR20","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/mcom.2019.1800155","volume":"57","author":"Y Hua","year":"2019","unstructured":"Hua, Y., Zhao, Z., Li, R., et al.: Deep learning with long short-term memory for time series prediction. IEEE Commun. Mag. 57(6), 114\u2013119 (2019). https:\/\/doi.org\/10.1109\/mcom.2019.1800155","journal-title":"IEEE Commun. Mag."}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63007-2_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:12:21Z","timestamp":1606090341000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-63007-2_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030630065","9783030630072"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63007-2_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"23 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Da Nang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}