  Time series methods are of fundamental importance in virtually any field of
science that deals with temporally structured data. Recently, there has been a
surge of deterministic transformer models with time series-specific
architectural biases. In this paper, we go in a different direction by
introducing tsGT, a stochastic time series model built on a general-purpose
transformer architecture. We focus on using a well-known and theoretically
justified rolling window backtesting and evaluation protocol. We show that tsGT
outperforms the state-of-the-art models on MAD and RMSE, and surpasses its
stochastic peers on QL and CRPS, on four commonly used datasets. We complement
these results with a detailed analysis of tsGT's ability to model the data
distribution and predict marginal quantile values.
