Demystifying interactions between temporal patterns of different scales is
fundamental to precise long-range time series forecasting. However, previous
works lack the ability to model high-order interactions. To promote more
comprehensive pattern interaction modeling for long-range time series
forecasting, we propose a Multi-Scale Hypergraph Transformer (MSHyper)
framework. Specifically, a multi-scale hypergraph is introduced to provide
foundations for modeling high-order pattern interactions. Then by treating
hyperedges as nodes, we also build a hyperedge graph to enhance hypergraph
modeling. In addition, a tri-stage message passing mechanism is introduced to
aggregate pattern information and learn the interaction strength between
temporal patterns of different scales. Extensive experiments on five real-world
datasets demonstrate that MSHyper achieves state-of-the-art performance,
reducing prediction errors by an average of 8.73% and 7.15% over the best
baseline in MSE and MAE, respectively.

使用多尺度超图转换器（MSHyper）框架，提出了一种更全面的模式交互建模方法，通过引入多尺度超图和超边图来建模高阶模式交互，同时引入三阶段消息传递机制来聚合模式信息和学习不同尺度时间模式之间的交互强度。实验证明，MSHyper 在五个真实世界数据集上取得了最先进的性能，相对于最佳基线方法，在均方误差和平均绝对误差分别平均减少了 8.73% 和 7.15% 的预测误差。

MSHyper：多尺度超图变换器用于长程时间序列预测

MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series  Forecasting

Long-range time series forecasting is usually based on one of two existing
forecasting strategies: Direct Forecasting and Iterative Forecasting, where the
former provides low bias, high variance forecasts and the latter leads to low
variance, high bias forecasts. In this paper, we propose a new forecasting
strategy called Generative Forecasting (GenF), which generates synthetic data
for the next few time steps and then makes long-range forecasts based on
generated and observed data. We theoretically prove that GenF is able to better
balance the forecasting variance and bias, leading to a much smaller
forecasting error. We implement GenF via three components: (i) a novel
conditional Wasserstein Generative Adversarial Network (GAN) based generator
for synthetic time series data generation, called CWGAN-TS. (ii) a transformer
based predictor, which makes long-range predictions using both generated and
observed data. (iii) an information theoretic clustering algorithm to improve
the training of both the CWGAN-TS and the transformer based predictor. The
experimental results on five public datasets demonstrate that GenF
significantly outperforms a diverse range of state-of-the-art benchmarks and
classical approaches. Specifically, we find a 5% - 11% improvement in
predictive performance (mean absolute error) while having a 15% - 50% reduction
in parameters compared to the benchmarks. Lastly, we conduct an ablation study
to further explore and demonstrate the effectiveness of the components
comprising GenF.

本文提出一种基于生成对抗网络及变换器的长期时间序列预测方法 (GenF), 证明其在预测方差和偏差上更加平衡，实验数据显示该方法在五个公共数据集上表现显著优于其他方法。