Wednesday, May 1, 2024, 2pm

The widespread adoption of time series machine learning (ML) models faces multiple challenges involving data, modeling and evaluation.Data. Modern ML models depend on copious amounts of cohesive and reliably annotated data for training and evaluation. However, labeled data is not always available and reliable, and can also be dispersed across different locations. We propose systematic solutions to making time series data ML-ready.

Modeling. Most current time series ML models are built, trained and evaluated on individual datasets from a specific application domain. Thus, to build an effective model for a particular application scenario, substantial effort, time, and domain expertise are required to develop a successful task-specific design. We propose to partially address this limitation by developing large pre-trained foundation models for time series, to ease development of useful models across diverse application domains with limited resources, data and labels.

Evaluation. Currently, time series models are commonly evaluated using relatively small, specific and highly tailored benchmarks, which may obfuscate assessment of their performance. We highlight the gaps in evaluation techniques and propose addressing the most important of them through comprehensive, multi-metric assessment.

In summary, this thesis aims to democratize time series artificial intelligence by simplifying and accelerating development of models, while improving their performance in real-world application scenarios facing resource constraints and imperfect data.

Thesis Committee:
Artur Dubrawski (Chair)
Jean Oh
Barnabás Póczos
Frederic Sala (University of Wisconsin-Madison)
Laurent Callot (Amazon)

Additional Information

In Person and Zoom Participation. See announcement.

Event Type: Thesis Proposals
Room Number: In Person and Virtual - ET
Building: Mauldin Auditorium, Newell-Simon 1305 and Zoom
Speaker's Name: MONONITO GOSWAMI
Speaker Website: mononitogoswami.github.io
Speaker's Professional Title: Ph.D. Student, Robotics Institute, Carnegie Mellon University
Talk Title: Towards Pragmatic Time Series Intelligence
For More Information: lyonsmuth@cmu.edu
Affiliations: Robotics Institute (RI)
Organization(s): SCS
Event Website Title: Event Website
Event Website URL: www.ri.cmu.edu…