Master Calendar

Abstract:Multivariate time series (MTS) analysis is critical for modeling complex temporal systems in many range of real-world domains, including finance, healthcare, and climate science. However, the high dimensionality, evolving dependencies, and non-stationary dynamics of MTS data pose significant challenges for building robust and generalizable forecasting models. Traditional statistical and deep learning methods for time series forecasting have largely focused on modeling temporal signals, often overlooking the knowledge that could improve model forecasting accuracy and adaptability. To bridge this gap, we propose knowledge informed time series analysis.The first work explores the external structural knowledge that captures the inter-variable relationships for continual multivariate time series forecasting. In dynamic temporal systems where the inter-variate dependencies shift as new regimes emerge, our method effectively guides the model to identify and leverage structural knowledge for time series forecasting. By selectively replaying informative samples from the learned representation space, the model memorizes previously learned knowledge, maintaining strong forecasting performance across both past and newly encountered regimes. Recent efforts have explored the use of large language models (LLMs) for time series forecasting, in our second approach, we leverage the semantic knowledge embedded in their pre-trained spaces for time series forecasting via a cross-modal prompt learning framework. Our approach first tokenizes decomposed time series components into semantically meaningful patches, creating embeddings aligned with LLM token spaces. We then select semantic anchors and align them with time series embeddings by maximizing their cosine similarity in a shared representation space. These aligned anchors serve as dynamic prompts, conditioning the model to better handle diverse temporal patterns. Through extensive experiments on benchmark datasets, we show that our framework not only surpasses state-of-the-art baselines but also have the improved interpretability.Beyond the current exploration of structural and semantic knowledge in multivariate time series, we aim to further investigate RAG enhanced time series foundation models and cross-modal knowledge integration to advance time series analysis.

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

This sesion will cover options for creating and arranging materials in your HuskyCT Ultra Course View course in ways that minimize confusion for students. We will discuss various organizational strategies, such as how to develop a straightforward Course Content page, the different types of content options, using Learning Modules and Folders for organization, as well as controlling the visibility of content to students. We will also cover options for adding multimedia elements to your course such as Kaltura videos, as well integrating third-party platforms, aka LTIs.August 27, 11:00-12:00 Register - https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3561 (https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3561)

The fair takes place from 11:00 a.m. to 2:00 p.m.

Identify common components of games; •Discover the power of games as a tool for learning (and more); •Differentiate the steps of game creation; •Assess the applicability of games to your contexts; •Gather resources for designing/implementing your own games Register - https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3564 (https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3564)

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

GI Grand Rounds conferences take place weekly on Wednesdays at noon via Webex. Please contact Amy Pallotti to be added to detailed conference announcement emails.

MEDS 6497 Neuroscience Journal Club Introduction To Neuroscience Journal Club Fall 2025 Course Co-Directors: Paola Bargagna-Mohan: bargagna-mohan@uchc.edu (mailto:bargagna-mohan@uchc.edu) Alice Burghard: burghard@uchc.edu (mailto:burghard@uchc.edu)

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

Rubrics are an assessment tool that promote consistent and reduced bias grading. helps instructors and students focus attention on the important aspects of an assignment. They are designed to help students understand what is at stake in an assignment, what to work toward, and how they will be assessed. For instructors, rubrics can serve as feedback on assignments, focus revisions, and motivate students to improve. This introductory workshop explores the elements of a rubric, methods for developing effective rubrics, and how to apply them within your courses.August 27, 12:30-1:30 Register - https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3520 (https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3520)

Join us for our 4th annual Welcome Back Block Party and Resource Fair. Food trucks, DJ, games, and learn about campus and local resources. Free for UConn Hartford students, faculty and staff. Event bracelet required - more information to follow about bracelet distribution. Event will take place rain or shine!   This event is co-sponsored by UConn Hartford, UConn Hartford Undergraduate Student Government, the School of Public Policy and the School of Business GBLC.

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).