tsGT: Stochastic Time Series Modeling With Transformer
<|reference_start|>tsGT: Stochastic Time Series Modeling With Transformer: 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.<|reference_end|>
@article{kuciński2024tsgt:,
    title={tsGT: Stochastic Time Series Modeling With Transformer},
    author={{L}ukasz Kuci'nski, Witold Drzewakowski, Mateusz Olko, Piotr
  Kozakowski, {L}ukasz Maziarka, Marta Emilia Nowakowska, {L}ukasz Kaiser,
  Piotr Mi{l}o's},
    journal={arXiv preprint arXiv:2403.05713},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05713},
    primaryClass={cs.LG}
}