MGCP：一种基于多粒度相关性的多元时间序列预测网络
MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series
AI总结：
针对多元时间序列预测中多粒度相关性难以同时学习的问题，提出MGCP网络，通过自适应傅里叶神经算子和图卷积网络提取细、中粒度特征，并利用对抗训练优化粗粒度预测，显著提升了预测性能。
摘要：
多元时间序列预测在日常生活中被广泛使用，由于多粒度级别上存在复杂的相关性，这带来了重大挑战。遗憾的是，当前大多数时间序列预测模型未能同时学习多元时间序列在多粒度级别上的相关性，导致性能次优。为了解决这个问题，我们提出了一种基于多粒度相关性的预测（MGCP）网络，该网络同时考虑了三个粒度级别上的相关性以增强预测性能。具体而言，MGCP利用自适应傅里叶神经算子和图卷积网络来学习全局时空相关性和序列间相关性，从而能够在细粒度和中粒度级别上从多元时间序列中提取潜在特征。此外，MGCP采用对抗训练，结合基于注意力机制的预测器和条件判别器来优化粗粒度级别的预测结果，确保生成的预测结果与实际数据分布之间具有高保真度。最后，我们在真实世界的基准数据集上将MGCP与几种最先进的时间序列预测算法进行了比较，我们的结果证明了所提出模型的通用性和有效性。
英文摘要：
Multivariate time series prediction is widely used in daily life, which poses significant challenges due to the complex correlations that exist at multi-grained levels. Unfortunately, the majority of current time series prediction models fail to simultaneously learn the correlations of multivariate time series at multi-grained levels, resulting in suboptimal performance. To address this, we propose a Multi-Grained Correlations-based Prediction (MGCP) Network, which simultaneously considers the correlations at three granularity levels to enhance prediction performance. Specifically, MGCP utilizes Adaptive Fourier Neural Operators and Graph Convolutional Networks to learn the global spatiotemporal correlations and inter-series correlations, enabling the extraction of potential features from multivariate time series at fine-grained and medium-grained levels. Additionally, MGCP employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level, ensuring high fidelity between the generated forecast results and the actual data distribution. Finally, we compare MGCP with several state-of-the-art time series prediction algorithms on real-world benchmark datasets, and our results demonstrate the generality and effectiveness of the proposed model.
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。
