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 $\mathtt{tsGT}$, 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 $\mathtt{tsGT}$ 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
$\mathtt{tsGT}$'s ability to model the data distribution and predict marginal
quantile values.

本论文介绍了一种基于通用 Transformer 架构的随机时间序列模型 tsGT，并使用滚动窗口回测和评估协议展示了其在四个常用数据集上在 MAD 和 RMSE 上优于现有模型，在 QL 和 CRPS 上超越了其随机同行模型，并补充了对 tsGT 建模数据分布和预测边际分位值能力的详细分析。