Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimensions)—such as in the case of clinical patient data.
By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series.
The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series.
Sparse and irregularly sampled multivariate time series are common in clinical, climate, financial and many other domains.
Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation.
Vertical FIT (FIT-V) is a variant of FIT which also models the relationship between different temporal signals while creating the informative dense representations for the signal.
We postulate that fine temporal detail, e. g., whether a series of blood tests are completed at once or in rapid succession should not alter predictions based on this data.