TACTiS: Transformer-Attentional Copulas for Time Series
- URL: http://arxiv.org/abs/2202.03528v1
- Date: Mon, 7 Feb 2022 21:37:29 GMT
- Title: TACTiS: Transformer-Attentional Copulas for Time Series
- Authors: Alexandre Drouin, \'Etienne Marcotte, Nicolas Chapados
- Abstract summary: estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance.
We propose a versatile method that estimates joint distributions using an attention-based decoder.
We show that our model produces state-of-the-art predictions on several real-world datasets.
- Score: 76.71406465526454
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The estimation of time-varying quantities is a fundamental component of
decision making in fields such as healthcare and finance. However, the
practical utility of such estimates is limited by how accurately they quantify
predictive uncertainty. In this work, we address the problem of estimating the
joint predictive distribution of high-dimensional multivariate time series. We
propose a versatile method, based on the transformer architecture, that
estimates joint distributions using an attention-based decoder that provably
learns to mimic the properties of non-parametric copulas. The resulting model
has several desirable properties: it can scale to hundreds of time series,
supports both forecasting and interpolation, can handle unaligned and
non-uniformly sampled data, and can seamlessly adapt to missing data during
training. We demonstrate these properties empirically and show that our model
produces state-of-the-art predictions on several real-world datasets.
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