HDT：用于多变量时间序列预测的分层离散Transformer
HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting
AI总结：
针对高维多变量时间序列预测中生成模型扩展难、预测长度受限的问题，提出HDT分层离散Transformer框架，通过离散token表示和分层建模延长预测长度并提升性能，实验验证其有效性。
摘要：
生成模型在多变量时间序列预测（MTS）中受到广泛关注，尤其是因其能够生成高保真样本。预测多变量时间序列的概率分布是一项具有挑战性但实用的任务。尽管最近已有一些尝试来处理该任务，但仍存在两大挑战：1）一些现有的生成方法在高维多变量时间序列预测中表现不佳，难以扩展到更高维度；2）固有的高维多变量属性限制了现有生成模型的预测长度。在本文中，我们指出离散token表示可以以更快的推理时间对高维MTS进行建模，并且利用目标自身的长期趋势进行预测可以在保证高精度的同时延长预测长度。基于此，我们提出了一种名为分层离散Transformer（HDT）的向量量化框架，该框架通过l2归一化增强的向量量化策略将时间序列建模为离散token表示，从而将MTS预测转化为离散token生成任务。为解决生成模型在长期预测中的局限性，我们提出了分层离散Transformer：该模型在低层捕捉目标的离散长期趋势，并将此趋势作为条件在高层生成目标的离散表示，引入目标自身的特征以延长高维MTS中的预测长度。在五个常用MTS数据集上的大量实验验证了我们所提方法的有效性。
英文摘要：
Generative models have gained significant attention in multivariate time series forecasting (MTS), particularly due to their ability to generate high-fidelity samples. Forecasting the probability distribution of multivariate time series is a challenging yet practical task. Although some recent attempts have been made to handle this task, two major challenges persist: 1) some existing generative methods underperform in high-dimensional multivariate time series forecasting, which is hard to scale to higher dimensions; 2) the inherent high-dimensional multivariate attributes constrain the forecasting lengths of existing generative models. In this paper, we point out that discrete token representations can model high-dimensional MTS with faster inference time, and forecasting the target with long-term trends of itself can extend the forecasting length with high accuracy. Motivated by this, we propose a vector quantized framework called Hierarchical Discrete Transformer (HDT) that models time series into discrete token representations with l2 normalization enhanced vector quantized strategy, in which we transform the MTS forecasting into discrete tokens generation. To address the limitations of generative models in long-term forecasting, we propose a hierarchical discrete Transformer. This model captures the discrete long-term trend of the target at the low level and leverages this trend as a condition to generate the discrete representation of the target at the high level that introduces the features of the target itself to extend the forecasting length in high-dimensional MTS. Extensive experiments on five popular MTS datasets verify the effectiveness of our proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
