Transformers for Time Series Data
Faculty: Zach Ives
with Jiaming Liang, Lei Cao, Sam Madden, and Guoliang Li
Many key health biomarkers are captured in timeseries data:
- blood pressure, heart rate, EKG, EEG, CRP, …
We need artificial intelligence to handle this at scale:
- Can we train AI to detect important diagnostic or predictive events in time, across multi-modal and multi-channel timeseries data?
- Can we automatically cluster related phenomena in real-time?
- Can we build databases and digital biobanks of timeseries
symptoms, and instantly find similar patterns?
Our RITA algorithm uses transformers to address these tasks over multi-channel timeseries – and is currently being investigated for epileptic seizure prediction.
In progress: building a data platform that supports integration of embeddings and database data.