Continuous Time Evidential Distributions for Irregular Time Series
Taylor W. Killian Haoran Zhang Thomas Hartvigsen Ava P. Amini
Prevalent in many real-world settings such as healthcare, irregular time
series are challenging to formulate predictions from. It is difficult to infer
the value of a feature at any given time when observations are sporadic, as it
could take on a range of values depending on when it was last observed. To
characterize this uncertainty we present EDICT, a strategy that learns an
evidential distribution over irregular time series in continuous time. This
distribution enables well-calibrated and flexible inference of partially
observed features at any time of interest, while expanding uncertainty
temporally for sparse, irregular observations. We demonstrate that EDICT
attains competitive performance on challenging time series classification tasks
and enabling uncertainty-guided inference when encountering noisy data.