  Time series models aim for accurate predictions of the future given the past,
where the forecasts are used for important downstream tasks like business
decision making. In practice, deep learning based time series models come in
many forms, but at a high level learn some continuous representation of the
past and use it to output point or probabilistic forecasts. In this paper, we
introduce a novel autoregressive architecture, VQ-AR, which instead learns a
\emph{discrete} set of representations that are used to predict the future.
Extensive empirical comparison with other competitive deep learning models
shows that surprisingly such a discrete set of representations gives
state-of-the-art or equivalent results on a wide variety of time series
datasets. We also highlight the shortcomings of this approach, explore its
zero-shot generalization capabilities, and present an ablation study on the
number of representations. The full source code of the method will be available
at the time of publication with the hope that researchers can further
investigate this important but overlooked inductive bias for the time series
domain.
