Time series forecasting is crucial for many fields, such as disaster warning, weather prediction, and energy consumption. The Transformer-based models are considered to have revolutionized the field of sequence modeling. However, the complex temporal patterns of the time series hinder the model from mining reliable temporal dependencies. Furthermore, the autoregressive form of the Transformer introduces cumulative errors in the inference step. In this paper, we propose the probabilistic decomposition Transformer model that combines the Transformer with a conditional generative model, which provides hierarchical and interpretable probabilistic forecasts for intricate time series. The Transformer is employed to learn temporal patterns and implement primary probabilistic forecasts, while the conditional generative model is used to achieve non-autoregressive hierarchical probabilistic forecasts by introducing latent space feature representations. In addition, the conditional generative model reconstructs typical features of the series, such as seasonality and trend terms, from probability distributions in the latent space to enable complex pattern separation and provide interpretable forecasts. Extensive experiments on several datasets demonstrate the effectiveness and robustness of the proposed model, indicating that it compares favorably with the state of the art.
翻译:时间序列预测对灾害预警、天气预测和能源消耗等许多领域至关重要。 以变异器为基础的模型被认为使序列建模领域发生了革命性的变化。 然而,时间序列复杂的时间时间模式阻碍了采矿可靠时间依赖模式的模型。 此外,变异器的自动递减形式在推论步骤中引入了累积错误。 在本文中,我们提议了将变异器与一个有条件的变异模型相结合的概率分解变异器模型,该模型为复杂时间序列提供了等级和可解释的概率预测。变异器用于学习时间模式和实施主要概率预测,而有条件的变异模型则用于通过引入潜伏空间特征表来实现非潜移动性的等级概率概率预测。此外,有条件的变异模型还重建了该系列的典型特征,如季节性和趋势,从潜在空间的概率分布到复杂的模式分离,并提供可解释的预报。 在几个数据集上进行的广泛实验,展示了拟议模型的有效性和稳健性,表明该模型与艺术品的状态相对有利。