{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:29:15Z","timestamp":1743092955761,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030595845"},{"type":"electronic","value":"9783030595852"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59585-2_5","type":"book-chapter","created":{"date-parts":[[2020,9,13]],"date-time":"2020-09-13T16:02:35Z","timestamp":1600012955000},"page":"50-61","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Dual Layer Regression Model for Cross-border E-commerce Industry Sale and Hot Product Prediction"],"prefix":"10.1007","author":[{"given":"Wangda","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruifeng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,14]]},"reference":[{"issue":"6176","key":"5_CR1","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1126\/science.1248506","volume":"343","author":"D Lazer","year":"2014","unstructured":"Lazer, D., Kennedy, R., King, G., et al.: The parable of Google flu: traps in big data analysis. Science 343(6176), 1203\u20131205 (2014)","journal-title":"Science"},{"issue":"7232","key":"5_CR2","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1038\/nature07634","volume":"457","author":"J Ginsberg","year":"2009","unstructured":"Ginsberg, J., Mohebbi, M.H., Patel, R., et al.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012\u20131014 (2009)","journal-title":"Nature"},{"doi-asserted-by":"crossref","unstructured":"Liu, C., Hoi, S.C., Zhao, P., et al.: Online ARIMA algorithms for time series prediction. In: National Conference on Artificial Intelligence, pp. 1867\u20131873 (2016)","key":"5_CR3","DOI":"10.1609\/aaai.v30i1.10257"},{"doi-asserted-by":"crossref","unstructured":"Shi, Q., Yin, J., Cai, J., et al.: Block Hankel tensor ARIMA for multiple short time series forecasting. In: National Conference on Artificial Intelligence (2020)","key":"5_CR4","DOI":"10.1609\/aaai.v34i04.6032"},{"doi-asserted-by":"crossref","unstructured":"Araz, O.M., Bentley, D., Muelleman, R.L., et al.: Using Google flu trends data in forecasting influenza-like-illness related ed visits in Omaha, Nebraska. Am. J. Emerg. Med. 32(9), 1016\u20131023 (2014)","key":"5_CR5","DOI":"10.1016\/j.ajem.2014.05.052"},{"unstructured":"Zhang, S.Q., Zhou, Z.H.: Harmonic recurrent process for time series forecasting. In: European Conference on Artificial Intelligence (2020)","key":"5_CR6"},{"doi-asserted-by":"crossref","unstructured":"Box, G.E., Jenkins, G.M.: Some recent advances in forecasting and control. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 17(2), 91\u2013109 (1968)","key":"5_CR7","DOI":"10.2307\/2985674"},{"unstructured":"Peiguang, J., Yuting, S., Xiao, J., et al.: High-order temporal correlation model learning for time-series prediction. IEEE Trans. Cybern. 49, 1\u201313 (2018)","key":"5_CR8"},{"doi-asserted-by":"crossref","unstructured":"Box, G.E., Jenkins, G.M.: Time series analysis, forecasting and control. J. Am. Stat. Assoc. 134(3) (1971)","key":"5_CR9","DOI":"10.2307\/2344246"},{"issue":"8","key":"5_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."},{"doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merrienboer, B., Gulcehre, C., et al.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv: Computation and Language (2014)","key":"5_CR11","DOI":"10.3115\/v1\/D14-1179"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","key":"5_CR12","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Empirical Methods in Natural Language Processing, pp. 1746\u20131751 (2014)","key":"5_CR13","DOI":"10.3115\/v1\/D14-1181"},{"issue":"1","key":"5_CR14","doi-asserted-by":"publisher","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","volume":"8","author":"Z Che","year":"2017","unstructured":"Che, Z., Purushotham, S., Cho, K., et al.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 6085 (2017)","journal-title":"Sci. Rep."},{"doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W., Yang, Y., et al.: Modeling long- and short-term temporal patterns with deep neural networks. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 95\u2013104 (2018)","key":"5_CR15","DOI":"10.1145\/3209978.3210006"},{"doi-asserted-by":"crossref","unstructured":"Mikolov, T., Karafiat, M., Burget, L., et al.: Recurrent neural network based language model. In: Conference of the International Speech Communication Association, pp. 1045\u20131048 (2010)","key":"5_CR16","DOI":"10.21437\/Interspeech.2010-343"},{"unstructured":"Du, J., Gui, L., He, Y., et al.: A convolutional attentional neural network for sentiment classification. In: International Conference on Security. IEEE (2018)","key":"5_CR17"},{"unstructured":"Li, S., Jin, X., Xuan, Y., et al.: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In: Neural Information Processing Systems, pp. 5244\u20135254 (2019)","key":"5_CR18"},{"unstructured":"Neo, W., Bradley, G., Xue, B., et al.: Deep transformer models for time series forecasting: the influenza prevalence case. arXiv: Computation and Language (2020)","key":"5_CR19"},{"unstructured":"Bahdanau, D., Cho, K., Bengio, Y., et al.: Neural machine translation by jointly learning to align and translate. In: International Conference on Learning Representations (2015)","key":"5_CR20"},{"unstructured":"Chorowski, J., Bahdanau, D., Serdyuk, D., et al.: Attention-based models for speech recognition. In: Neural Information Processing Systems, pp. 577\u2013585 (2015)","key":"5_CR21"},{"unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Neural Information Processing Systems, pp. 6000\u20136010 (2017)","key":"5_CR22"},{"doi-asserted-by":"crossref","unstructured":"Salinas, D., Flunkert, V., Gasthaus, J., et al.: DeepAR: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. 36, 1181\u2013191 (2019)","key":"5_CR23","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"unstructured":"Rangapuram, S.S., Seeger, M., Gasthaus, J., et al.: Deep state space models for time series forecasting. In: Neural Information Processing Systems, pp. 7785\u20137794 (2018)","key":"5_CR24"},{"unstructured":"Jian, L., Chunlin, L., Lanping, Z., et al.: Research on sales information prediction system of e-commerce enterprises based on time series model. Inf. Syst. E-Bus. Manage. (2019)","key":"5_CR25"},{"doi-asserted-by":"crossref","unstructured":"Charles, M.: Marketing and e-commerce as tools of development in the Asia-Pacific region: a dual path. Int. Market. Rev. 21(3), 301\u2013320 (2004)","key":"5_CR26","DOI":"10.1108\/02651330410539639"},{"unstructured":"Kechyn, G., Yu, L., Zang, Y., et al.: Sales forecasting using WaveNet within the framework of the Kaggle competition. arXiv: Learning (2018)","key":"5_CR27"},{"doi-asserted-by":"crossref","unstructured":"Choi, T., Yu, Y., Au, K., et al.: A hybrid SARIMA wavelet transform method for sales forecasting. Decis. Support Syst. 51(1), 130\u2013140 (2011)","key":"5_CR28","DOI":"10.1016\/j.dss.2010.12.002"},{"doi-asserted-by":"crossref","unstructured":"Chen, S., Hwang, J.: Temperature prediction using fuzzy time series. Syst. Man Cybern. 30(2), 263\u2013275 (2000)","key":"5_CR29","DOI":"10.1109\/3477.836375"},{"doi-asserted-by":"crossref","unstructured":"Xiao, C., Chen, N., Hu, C., et al.: Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach. Remote Sens. Environ. (2019)","key":"5_CR30","DOI":"10.1016\/j.rse.2019.111358"},{"doi-asserted-by":"crossref","unstructured":"Ariyo, A.A., Adewumi, A.O., Ayo, C.K., et al.: Stock price prediction using the ARIMA model. In: International Conference on Computer Modelling and Simulation, pp. 106\u2013112 (2014)","key":"5_CR31","DOI":"10.1109\/UKSim.2014.67"},{"issue":"18","key":"5_CR32","doi-asserted-by":"publisher","first-page":"18569","DOI":"10.1007\/s11042-016-4159-7","volume":"76","author":"R Singh","year":"2017","unstructured":"Singh, R., Srivastava, S.: Stock prediction using deep learning. Multimed. Tools Appl. 76(18), 18569\u201318584 (2017)","journal-title":"Multimed. Tools Appl."},{"unstructured":"Sutskever, I., Vinyals, O., Le, Q.V., et al.: Sequence to sequence learning with neural networks. arXiv: Computation and Language (2014)","key":"5_CR33"},{"unstructured":"Ke, G., Meng, Q., Finley, T.W., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Neural Information Processing Systems, pp. 3149\u20133157 (2017)","key":"5_CR34"},{"key":"5_CR35","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.4028\/www.scientific.net\/AMM.373-375.1686","volume":"373","author":"CS Luo","year":"2013","unstructured":"Luo, C.S., Zhou, L.Y., Wei, Q.F.: Application of SARIMA model in cucumber price forecast. Appl. Mech. Mater. 373, 1686\u20131690 (2013)","journal-title":"Appl. Mech. Mater."},{"doi-asserted-by":"crossref","unstructured":"Zou, H., Hastie, T.: Addendum: regularization and variable selection via the elastic net. J. Roy. Stat. Soc. Ser. B 67(5), 768\u2013768 (2005)","key":"5_CR36","DOI":"10.1111\/j.1467-9868.2005.00527.x"}],"container-title":["Lecture Notes in Computer Science","Cognitive Computing \u2013 ICCC 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59585-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:03:59Z","timestamp":1726185839000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59585-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030595845","9783030595852"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59585-2_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Cognitive Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Honolulu, HI","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"22 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccc2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.thecognitivecomputing.org\/2020\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EDAS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20","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":"8","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":"2","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":"40% - 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","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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-10 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}