MSPredictor: A Multi-Scale Dynamic Graph Neural Network for Multivariate Time Series Prediction

In the field of multivariate time series prediction, capturing the dynamic relationships and complex cyclical patterns between sequences is key to improving prediction accuracy. To address this challenge, our paper introduces MSPredictor, a multi-scale dynamic graph neural network model, which uses Fast Fourier Transform for multi-scale decoupling in the frequency domain and employs Kolmogorov-Arnold Networks for multi-scale fusion, effectively extracting significant cyclical patterns. By decomposing the original series across different scales, MSPredictor accurately models complex cyclical patterns. To enhance the model's transparency and interpretability, we introduced the ClarityLens explanatory strategy, which employs visualization techniques to make the prediction process more transparent. Specifically, it displays the adjacency matrices learned at different scales, intuitively showing the dynamic correlations between series. We also visualized the proportion of different periods in the prediction results and the specific forecasting performance at each time scale. Extensive testing on multiple real-world datasets has demonstrated that the MSPredictor significantly outperforms existing benchmarks, validating its practicality and high transparency.