Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting

• URL: http://arxiv.org/abs/2403.05406v1
• Date: Fri, 8 Mar 2024 16:04:36 GMT
• Title: Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting
• Authors: Muyao Wang, Wenchao Chen, Bo Chen
• Abstract summary: We develop a powerful hierarchical probabilistic generative module to consider the non-stationarity and intrinsic characteristics within MTS. We then combine it with transformer for a well-defined variational generative dynamic model named Hierarchical Time series Variational Transformer (HTV-Trans) Being a powerful probabilistic model, HTV-Trans is utilized to learn expressive representations of MTS and applied to forecasting tasks.
• Score: 12.793705636683402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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