{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:08:19Z","timestamp":1743084499042,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819755516"},{"type":"electronic","value":"9789819755523"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-5552-3_7","type":"book-chapter","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:04:15Z","timestamp":1727679855000},"page":"103-118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TimeGAE: A Multivariate Time-Series Generation Method via\u00a0Graph Auto Encoder"],"prefix":"10.1007","author":[{"given":"Zhao","family":"Bai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangda","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Xi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuoming","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongfang","family":"Bie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"7_CR1","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of NIPS, pp. 6000\u20136010 (2017)"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep Residual Learning for Image Recognition. In: Proceedings of CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"7_CR3","unstructured":"Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of DLRS, pp. 7\u201310 (2016)"},{"key":"7_CR4","unstructured":"Han, J., Micheline, K.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2022)"},{"key":"7_CR5","unstructured":"Achiam, J., et al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"7_CR6","unstructured":"Nikolaidis, K., et al.: Augmenting physiological time series data: a case study for sleep apnea detection. In: Proceedings of ECML PKDD, pp. 376-399 (2019)"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Abdelfattah, S.M., et al.: Augmenting The Size of EEG datasets Using Generative Adversarial Networks. In: Proceedings of IJCNN, pp. 1\u20136 (2018)","DOI":"10.1109\/IJCNN.2018.8489727"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Harada, S., et al.: Biosignal data augmentation based on generative adversarial networks. In: Proceedings of EMBC, pp. 368\u2013371 (2018)","DOI":"10.1109\/EMBC.2018.8512396"},{"issue":"11","key":"7_CR9","doi-asserted-by":"publisher","first-page":"3226","DOI":"10.1109\/JBHI.2020.2979608","volume":"24","author":"D Kiyasseh","year":"2020","unstructured":"Kiyasseh, D., et al.: PlethAugment: GAN-Based PPG augmentation for medical diagnosis in low-resource settings. IEEE J. Biomed. Health Inform. 24(11), 3226\u20133235 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Snore-gans: Improving automatic snore sound classification with synthesized data (2019)","DOI":"10.1109\/JBHI.2019.2907286"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Umek, A., Toma\u017ei\u010d, S., Kos, A.: Wearable training system with real-time biofeedback and gesture user interface. Personal and Ubiquitous Computing, pp. 989\u2013998 (2015)","DOI":"10.1007\/s00779-015-0886-4"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Juvela, L., et al.: Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks. In: Proceedings of ICASSP, pp. 6915\u20136919 (2019)","DOI":"10.1109\/ICASSP.2019.8683271"},{"key":"7_CR13","unstructured":"Mogren, O.: C-RNN-GAN: Continuous recurrent neural networks with adversarial training. arXiv preprint arXiv:1611.09904 (2016)"},{"issue":"9","key":"7_CR14","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1080\/14697688.2020.1730426","volume":"20","author":"M Wiese","year":"2020","unstructured":"Wiese, M., Knobloch, R., Korn, R., Kretschmer, P.: Quant GANs: deep generation of financial time series. Quantitative Finance 20(9), 1419\u20131440 (2020)","journal-title":"Quantitative Finance"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Iwana BK, Uchida S.: An empirical survey of data augmentation for time series classification with neural networks. PLoS One, vol. 16, no. 7 (2021)","DOI":"10.1371\/journal.pone.0254841"},{"key":"7_CR16","first-page":"2672","volume":"3","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Adv. Neural. Inf. Process. Syst. 3, 2672\u20132680 (2014)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"7_CR17","unstructured":"Esteban, C., Hyland, S.L., R\u00e4tsch, G.: Real-valued (medical) time series generation with recurrent conditional GANs. arXiv preprint arXiv:1706.02633 (2017)"},{"key":"7_CR18","unstructured":"Ramponi, G., Protopapas, P., Brambilla, M., Janssen, R.: T-CGAN: Conditional generative adversarial network for data augmentation in noisy time series with irregular sampling. arXiv preprint arXiv:1811.08295 (2018)"},{"key":"7_CR19","unstructured":"Yoon, J., Jarrett, D., Van Der Schaar, M.: Time-series Generative Adversarial Networks. Advances in neural information processing systems (2019)"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Wu, Z., et al.: Connecting the dots: Multivariate time series forecasting with graph neural networks. In: Proceedings of KDD, pp. 753\u2013763 (2020)","DOI":"10.1145\/3394486.3403118"},{"key":"7_CR21","unstructured":"Shang, C., Chen, J., Bi, J.: Discrete graph structure learning for forecasting multiple time series. arXiv preprint arXiv:2101.06861 (2021)"},{"key":"7_CR22","unstructured":"Cao, D., et al.: Spectral temporal graph neural network for multivariate time-series forecasting. In: Proceedings of NeurIPS, pp. 17766\u201317778 (2020)"},{"key":"7_CR23","unstructured":"Xu, W., Liu, W., Bian, J., Yin, J., Liu, T.Y.: Instance-wise graph-based framework for multivariate time series forecasting. arXiv preprint arXiv:2109.06489 (2021)"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Deng, A., Hooi, B.: Graph neural network-based anomaly detection in multivariate time series. In: Proceedings of AAAI, pp. 4027\u20134035 (2021)","DOI":"10.1609\/aaai.v35i5.16523"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Zhao, H., et al.: Multivariate time-series anomaly detection via graph attention network. In: Proceedings of ICDM, pp. 841\u2013850 (2020)","DOI":"10.1109\/ICDM50108.2020.00093"},{"key":"7_CR26","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.procs.2018.03.046","volume":"129","author":"L Jiao","year":"2018","unstructured":"Jiao, L., et al.: Multi-sensor golf swing classification using deep CNN. Procedia Computer Science 129, 59\u201365 (2018)","journal-title":"Procedia Computer Science"},{"issue":"3","key":"7_CR27","doi-asserted-by":"publisher","first-page":"17","DOI":"10.4018\/JDM.2018070102","volume":"29","author":"L Jiao","year":"2018","unstructured":"Jiao, L., et al.: Towards real-time multi-sensor golf swing classification using deep CNNs. J. Database Manage. 29(3), 17\u201342 (2018)","journal-title":"J. Database Manage."},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Jiao, L., et al.: Golf swing classification with multiple deep convolutional neural networks. Int. J. Distributed Sensor Networks (2018)","DOI":"10.1177\/1550147718802186"},{"key":"7_CR29","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.enbuild.2017.01.083","volume":"140","author":"LM Candanedo","year":"2017","unstructured":"Candanedo, L.M., Feldheim, V., Deramaix, D.: Data driven prediction models of energy use of appliances in a low-energy house. Energy Buildings 140, 81\u201397 (2017)","journal-title":"Energy Buildings"},{"key":"7_CR30","doi-asserted-by":"crossref","unstructured":"Khosravi, I., Alavipanah, S.K.: A random forest-based framework for crop mapping using temporal, spectral, textural, and polarimetric observations. International Journal of Remote Sensing, pp. 1\u201331 (2019)","DOI":"10.1080\/01431161.2019.1601285"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Khosravi, I., Safari, A., Homayouni, S.: MSMD: maximum separability and minimum dependency feature selection for cropland classification from optical and radar data. Int. J. Remote Sensing (2018)","DOI":"10.1080\/01431161.2018.1425564"},{"key":"7_CR32","unstructured":"Desai, A., Freeman, C., Wang, Z., Beaver, I.: TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation (2021)"},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Li, X., Metsis, V., Wang, H., Ngu, A.H.: TTS-GAN: a transformer-based time-series generative adversarial network. In: Proceedings of AIME (2022)","DOI":"10.1007\/978-3-031-09342-5_13"},{"key":"7_CR34","unstructured":"Bryant, F.B., Yarnold, P.R.: Principal-components analysis and exploratory and confirmatory factor analysis. Reading and understanding multivariate statistics, pp. 99\u2013136 (1995)"},{"key":"7_CR35","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res., 2579\u20132605 (2008)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5552-3_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:07:23Z","timestamp":1727680043000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5552-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755516","9789819755523"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5552-3_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gifu","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}