Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting
<|reference_start|>Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting: The forecasting of Multivariate Time Series (MTS) has long been an important but challenging task. Due to the non-stationary problem across long-distance time steps, previous studies primarily adopt stationarization method to attenuate the non-stationary problem of the original series for better predictability. However, existing methods always adopt the stationarized series, which ignores the inherent non-stationarity, and has difficulty in modeling MTS with complex distributions due to the lack of stochasticity. To tackle these problems, we first develop a powerful hierarchical probabilistic generative module to consider the non-stationarity and stochastic characteristics within MTS, and then combine it with transformer for a well-defined variational generative dynamic model named Hierarchical Time series Variational Transformer (HTV-Trans), which recovers the intrinsic non-stationary information into temporal dependencies. Being a powerful probabilistic model, HTV-Trans is utilized to learn expressive representations of MTS and applied to forecasting tasks. Extensive experiments on diverse datasets show the efficiency of HTV-Trans on MTS forecasting tasks<|reference_end|>
@article{wang2024considering,
    title={Considering Nonstationary within Multivariate Time Series with
  Variational Hierarchical Transformer for Forecasting},
    author={Muyao Wang, Wenchao Chen, Bo Chen},
    journal={arXiv preprint arXiv:2403.05406},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05406},
    primaryClass={cs.LG cs.AI}
}