Representation Learning with Time Series Data

The proposed research aims at learning better representations for multivariate time series (MTS) data, which can be applied to various important real-life applications such as weather, traffic, and electricity forecasting. Better forecasting accuracies for these tasks could help with efficient risk aversion and decision making, and save costs for decision makers. The proposed research will look into current techniques for MTS data modeling and will be focusing on filling gaps in existing research to further improve forecasting results. The proposed research will explore novel modeling approaches that can effectively capture the important and distinct characteristics of MTS data, and will develop approaches that can transfer between MTS data in different domains.

Faculty Supervisor:

Rahul G. Krishnan

Student:

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

Artificial Intelligence; Information and Communications Technology; Technology

University:

University of Toronto

Program: