Probabilistic Forecasting of Irregular Time Series via Conditional Flows
Vijaya Krishna Yalavarthi Randolf Scholz Stefan Born Lars Schmidt-Thieme
Probabilistic forecasting of irregularly sampled multivariate time series
with missing values is an important problem in many fields, including health
care, astronomy, and climate. State-of-the-art methods for the task estimate
only marginal distributions of observations in single channels and at single
timepoints, assuming a fixed-shape parametric distribution. In this work, we
propose a novel model, ProFITi, for probabilistic forecasting of irregularly
sampled time series with missing values using conditional normalizing flows.
The model learns joint distributions over the future values of the time series
conditioned on past observations and queried channels and times, without
assuming any fixed shape of the underlying distribution. As model components,
we introduce a novel invertible triangular attention layer and an invertible
non-linear activation function on and onto the whole real line. We conduct
extensive experiments on four datasets and demonstrate that the proposed model
provides $4$ times higher likelihood over the previously best model.