  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.
