MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series

TL;DR

The paper introduces MGCP, a novel neural network that captures multi-grained correlations in multivariate time series to improve prediction accuracy across different granularities.

Contribution

It proposes a multi-grained correlation learning framework combining Fourier neural operators and graph convolutions, with adversarial training for enhanced multilevel prediction performance.

Findings

MGCP outperforms state-of-the-art models on benchmark datasets.

The model effectively captures correlations at multiple granularities.

Results demonstrate the generality and robustness of MGCP.

Abstract

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.