ProFITi: Probabilistic Forecasting of Irregular Time Series via Conditional Flows

Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme

Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)

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Keywords: Time Series, Irregularly Sampled Time Series with Missing Values, Probabilistic Forecasting, Normalizing Flows, Conditional Normalizing Flows

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Abstract:

Probabilistic forecasting of irregularly sampled multivariate time series with missing values
is an important problem in many fields, including astronomy, finance, and healthcare.
Traditional methods for this task often rely on differential equations
based models and make an assumption on the target distribution.
In recent years, normalizing flow models have emerged as a promising approach for
density estimation and uncertainty quantification, offering a flexible framework
that can capture complex dependencies.
In this work, we propose a novel model ProFITi
for probabilistic forecasting of irregular time series with missing values using conditional normalizing flows.
In this approach, the model learns a {joint} probability distribution over the future values of the time series
conditioned on the past observations and query (future) time-channel information.
As components of our model, 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 $3$ datasets,
and demonstrate that the proposed model, ProFITi, provides significantly better forecasting likelihoods compared to the existing baseline models.
Specifically, on average, ProFITi provides $4$ times higher likelihood over the previously best model.

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Submission Number: 1300