{"id":7781,"date":"2025-09-09T03:46:48","date_gmt":"2025-09-09T03:46:48","guid":{"rendered":"https:\/\/iccies.tdtu.edu.vn\/2026\/?page_id=7781"},"modified":"2026-08-26T03:53:01","modified_gmt":"2026-08-26T03:53:01","slug":"time-series-analytics","status":"publish","type":"page","link":"https:\/\/iccies.tdtu.edu.vn\/2027\/time-series-analytics\/","title":{"rendered":"Time Series Analytics"},"content":{"rendered":"\n<h2 class=\"wp-block-heading has-text-align-center\">Time Series Analytics (TSA)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">I. <strong>Objectives<\/strong><\/h3>\n\n\n\n<p>The TSA session will explore theoretical advances and practical applications in sequential and temporal data analysis. It will cover both traditional approaches and recent advances in deep learning, foundation models, and generative and graph-based methods for time series. Topics include forecasting, anomaly\/change-point detection, imputation, classification, causal inference, and representation learning, especially under challenges such as missing data, irregular sampling, non-stationarity, distribution shift, and scalability. The session also welcomes work on interpretable, trustworthy, and resource-efficient time-series models, from spectral and physics-informed approaches (e.g., Koopman operator methods) to large pretrained forecasting models. Application domains include healthcare, finance, energy, IoT, transportation, and climate science.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">II. Scope<\/h3>\n\n\n\n<p>List of Relevant Topics<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Forecasting: univariate, multivariate, probabilistic, hierarchical, global models<\/li>\n\n\n\n<li>Time-series foundation models and LLM-based forecasting (zero-shot and few-shot settings)<\/li>\n\n\n\n<li>Transformer-based, state-space (e.g., Mamba\/S4), and hybrid architectures for time series<\/li>\n\n\n\n<li>Koopman operator, spectral, and physics-informed methods for temporal dynamics<\/li>\n\n\n\n<li>Diffusion and generative models for time-series generation, imputation, and simulation<\/li>\n\n\n\n<li>Graph neural networks for multivariate and spatio-temporal data<\/li>\n\n\n\n<li>Anomaly and change-point detection in batch and streaming data<\/li>\n\n\n\n<li>Missing-value imputation, irregular sampling, data augmentation<\/li>\n\n\n\n<li>Self-supervised and contrastive representation learning<\/li>\n\n\n\n<li>Causal time-series analysis, counterfactuals, uplift modeling<\/li>\n\n\n\n<li>Classification, clustering, segmentation, motif discovery<\/li>\n\n\n\n<li>Federated, privacy-preserving, and edge time-series learning<\/li>\n\n\n\n<li>Robustness, uncertainty quantification, interpretability, and evaluation frameworks<\/li>\n\n\n\n<li>Applications in energy, healthcare, finance, IoT, transportation, and climate<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">III. Submission link: <a href=\"https:\/\/iccies.tdtu.edu.vn\/2027\/how-to-submit\/\" data-type=\"page\" data-id=\"6712\">click here<\/a><\/h3>\n\n\n\n<p>Please select the Track\/Session: \u201cTime Series Analytics (TSA)\u201d during submission<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">IV. <strong>Session Organizers<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Dr. Nguyen Ngoc Phien \u2014 Ton Duc Thang University (TDTU). Email: nguyenngocphien@tdtu.edu.vn<\/li>\n\n\n\n<li>Dr. Tran Trung Tin \u2014 Ton Duc Thang University (TDTU). Email: trantrungtin@tdtu.edu.vn<\/li>\n\n\n\n<li>Dr. Duong Thi Thuy Van \u2014 Ton Duc Thang University (TDTU). Email: duongthithuyvan@tdtu.edu.vn<\/li>\n\n\n\n<li>Assoc. Prof. Duong Tuan Anh \u2014 HCMC University of Foreign Languages &#8211; Information Technology (HUFLIT)<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Time Series Analytics (TSA) I. Objectives The TSA session will explore theoretical advances and practical applications in sequential and temporal data analysis. It will cover both traditional approaches and recent advances in deep learning, foundation models, and generative and graph-based methods for time series. Topics include forecasting, anomaly\/change-point detection, imputation, classification, causal inference, and representation &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/iccies.tdtu.edu.vn\/2027\/time-series-analytics\/\" class=\"more-link\">Read more<span class=\"screen-reader-text\"> &#8220;Time Series Analytics&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/no-title.php","meta":{"footnotes":""},"class_list":["post-7781","page","type-page","status-publish","hentry"],"featured_media_urls":[],"_links":{"self":[{"href":"https:\/\/iccies.tdtu.edu.vn\/2027\/wp-json\/wp\/v2\/pages\/7781"}],"collection":[{"href":"https:\/\/iccies.tdtu.edu.vn\/2027\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/iccies.tdtu.edu.vn\/2027\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/iccies.tdtu.edu.vn\/2027\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/iccies.tdtu.edu.vn\/2027\/wp-json\/wp\/v2\/comments?post=7781"}],"version-history":[{"count":7,"href":"https:\/\/iccies.tdtu.edu.vn\/2027\/wp-json\/wp\/v2\/pages\/7781\/revisions"}],"predecessor-version":[{"id":8105,"href":"https:\/\/iccies.tdtu.edu.vn\/2027\/wp-json\/wp\/v2\/pages\/7781\/revisions\/8105"}],"wp:attachment":[{"href":"https:\/\/iccies.tdtu.edu.vn\/2027\/wp-json\/wp\/v2\/media?parent=7781"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}