TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including forecasting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based atten...
Saved in:
Main Authors | , , , , |
---|---|
Format | Journal Article |
Language | English |
Published |
02.10.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | We introduce a new model for multivariate probabilistic time series
prediction, designed to flexibly address a range of tasks including
forecasting, interpolation, and their combinations. Building on copula theory,
we propose a simplified objective for the recently-introduced transformer-based
attentional copulas (TACTiS), wherein the number of distributional parameters
now scales linearly with the number of variables instead of factorially. The
new objective requires the introduction of a training curriculum, which goes
hand-in-hand with necessary changes to the original architecture. We show that
the resulting model has significantly better training dynamics and achieves
state-of-the-art performance across diverse real-world forecasting tasks, while
maintaining the flexibility of prior work, such as seamless handling of
unaligned and unevenly-sampled time series. Code is made available at
https://github.com/ServiceNow/TACTiS. |
---|---|
DOI: | 10.48550/arxiv.2310.01327 |