基于超网络的多变量时间序列预测
HN-MVTS: HyperNetwork-based Multivariate Time Series Forecasting
AI总结：
本文提出基于超网络的多变量时间序列预测模型HN-MVTS，通过生成先验和任意神经网络结合，提升预测精度和泛化能力。
摘要：
准确预测多变量时间序列数据仍然是一项严峻的挑战，尤其是在现实场景中时间依赖性日益复杂的情况下。虽然基于神经网络的模型在这一领域取得了显著的成功，但复杂的通道依赖模型往往在性能上劣于不考虑组件之间关系的通道独立模型，后者由于容量小而具有较高的鲁棒性。在本工作中，我们提出了一种新的架构HN-MVTS，该架构集成了基于超网络的生成先验和任意神经网络预测模型。该超网络的输入是一个可学习的时间序列组件嵌入矩阵。为了限制新参数的数量，超网络学习生成目标预测网络最后一层的权重，作为数据自适应的正则化器，以提高泛化能力和长距离预测准确性。超网络仅在训练过程中使用，因此与基预测模型相比，推理时间不会增加。在八个基准数据集上的广泛实验表明，将HN-MVTS应用于最先进的模型（DLinear、PatchTST、TSMixer等）通常会提高其性能。我们的发现表明，由超网络驱动的参数化方法为在复杂场景中增强现有预测技术提供了一个有前途的方向。
英文摘要：
Accurate forecasting of multivariate time series data remains a formidable challenge, particularly due to the growing complexity of temporal dependencies in real-world scenarios. While neural network-based models have achieved notable success in this domain, complex channel-dependent models often suffer from performance degradation compared to channel-independent models that do not consider the relationship between components but provide high robustness due to small capacity. In this work, we propose HN-MVTS, a novel architecture that integrates a hypernetwork-based generative prior with an arbitrary neural network forecasting model. The input of this hypernetwork is a learnable embedding matrix of time series components. To restrict the number of new parameters, the hypernetwork learns to generate the weights of the last layer of the target forecasting networks, serving as a data-adaptive regularizer that improves generalization and long-range predictive accuracy. The hypernetwork is used only during the training, so it does not increase the inference time compared to the base forecasting model. Extensive experiments on eight benchmark datasets demonstrate that application of HN-MVTS to the state-of-the-art models (DLinear, PatchTST, TSMixer, etc.) typically improves their performance. Our findings suggest that hypernetwork-driven parameterization offers a promising direction for enhancing existing forecasting techniques in complex scenarios.
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。
