tsGT: Stochastic Time Series Modeling With Transformer
arxiv(2024)
摘要
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.
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