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.

提出了一种基于多粒度关联的预测网络 (MGCP)，该网络同时考虑了多粒度水平上的多元时间序列的关联，从而提高了预测性能。通过使用自适应傅里叶神经算子和图卷积网络来学习全局时空关联和跨系列关联，MGCP 能够从多元时间序列中提取细粒度和中等粒度水平的潜在特征。此外，MGCP 还利用带有注意机制预测器和条件鉴别器的对抗训练来优化粗粒度水平上的预测结果，并确保生成的预测结果与实际数据分布的高保真度。最后，将 MGCP 与几种最先进的时间序列预测算法在真实世界的基准数据集上进行比较，结果表明了所提模型的广泛适用性和有效性。